CN112945988A - Lens defect detection system and detection method - Google Patents
Lens defect detection system and detection method Download PDFInfo
- Publication number
- CN112945988A CN112945988A CN202110158564.8A CN202110158564A CN112945988A CN 112945988 A CN112945988 A CN 112945988A CN 202110158564 A CN202110158564 A CN 202110158564A CN 112945988 A CN112945988 A CN 112945988A
- Authority
- CN
- China
- Prior art keywords
- light source
- image
- lens
- mat
- defect detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000007547 defect Effects 0.000 title claims abstract description 202
- 238000001514 detection method Methods 0.000 title claims abstract description 142
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 claims description 24
- 230000009466 transformation Effects 0.000 claims description 24
- 238000009792 diffusion process Methods 0.000 claims description 20
- 241000519995 Stachys sylvatica Species 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 16
- 230000008859 change Effects 0.000 claims description 14
- 230000008030 elimination Effects 0.000 claims description 13
- 238000003379 elimination reaction Methods 0.000 claims description 13
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000007689 inspection Methods 0.000 claims description 10
- 239000011324 bead Substances 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 239000012528 membrane Substances 0.000 claims description 7
- 239000003086 colorant Substances 0.000 claims description 6
- 230000009286 beneficial effect Effects 0.000 claims description 5
- 230000000295 complement effect Effects 0.000 claims description 5
- 230000003287 optical effect Effects 0.000 claims description 5
- 239000000463 material Substances 0.000 claims description 3
- 230000003746 surface roughness Effects 0.000 claims description 2
- 238000003384 imaging method Methods 0.000 description 16
- 238000005286 illumination Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 7
- 206010027146 Melanoderma Diseases 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000002950 deficient Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000002834 transmittance Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 238000011895 specific detection Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 210000002268 wool Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/958—Inspecting transparent materials or objects, e.g. windscreens
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8806—Specially adapted optical and illumination features
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8806—Specially adapted optical and illumination features
- G01N2021/8812—Diffuse illumination, e.g. "sky"
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8806—Specially adapted optical and illumination features
- G01N2021/8822—Dark field detection
- G01N2021/8825—Separate detection of dark field and bright field
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Testing Of Optical Devices Or Fibers (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention relates to a lens defect detection system and a lens defect detection method, wherein the lens defect detection system comprises a camera unit (2), a coaxial light source (3), a dome light source (4), an annular light source (5) and a backlight light source (6) which are sequentially arranged, the camera unit (2), the coaxial light source (3), the dome light source (4), the annular light source (5) and the backlight light source (6) are coaxially arranged, and the dome light source (4) and the annular light source (5) are arranged at intervals. The lens defect detection system and the detection method have the advantages of high detection accuracy, wide application range and low omission factor.
Description
Technical Field
The invention relates to the technical field of optics, in particular to a lens defect detection system and a detection method.
Background
The application range of the mobile phone camera module in the current market is more and more extensive, the quality requirement of the camera module by a client is higher and higher, and the surface defect of the lens in the camera module greatly determines the quality of the image formed by the mobile phone camera module. The main optical form of the mobile phone lens is an aspheric lens, and in order to ensure the imaging effect of the lens, the defect detection is usually performed on the upper surface and the lower surface of the lens. Due to the wide variety of defects, the requirements on the optical system and the detection algorithm of the automatic detection equipment are high. The existing lens detection equipment in the industry usually adopts an upper annular light source and a lower annular light source for illumination, and adopts a scheme of single image taking, and the scheme has the following defects:
1. the adoption of the upper and lower light sources for illumination can cause the poor imaging effect of partial shallow dirt, shallow scratch and black spots and easy detection omission.
2. By adopting the scheme of single drawing, defects such as white spots, demoulding and the like cannot be effectively distinguished, and especially for demoulding with small size (within 30 mu m), the omission ratio is high.
3. When the lens with larger thickness is detected due to the limitation of the depth of field of the lens, partial areas of the lens are out of focus, so that defects such as white spots, stripping and the like are imaged in a virtual mode to cause an over-detection problem, and imaging effects of shallow dirt and scratches are weakened to cause a missing detection problem.
4. The defect detection adopts a segmentation method such as a fixed threshold or a dynamic threshold, and the detection omission rate of defects such as shallow dirt, demoulding and the like is high.
Disclosure of Invention
The invention aims to solve the problems and provides a lens defect detection system and a defect detection method, which solve the problem of high omission factor of the existing detection equipment and detection method.
In order to achieve the above object, the present invention provides a lens defect detecting system, which includes a camera unit, a coaxial light source, a dome light source, an annular light source and a backlight light source, which are sequentially arranged, wherein the camera unit, the coaxial light source, the dome light source, the annular light source and the backlight light source are coaxially arranged, and the dome light source and the annular light source are arranged alternately.
According to one aspect of the invention, the dome light source is provided with an attachment plate on which the coaxial light source is fixedly supported.
According to one aspect of the invention, the light emitting surface of the coaxial light source and the light through hole on the dome light source are in a proportional relationship of two times and more.
According to one aspect of the invention, the camera unit comprises a support plate, a fixing plate mounted on the support plate, an image sensor coaxially arranged on the fixing plate in sequence, a telecentric lens and a driving device for driving the fixing plate to move.
According to an aspect of the present invention, the backlight light source includes a diffusion plate whose color is set to a complementary color to the light source band colors of the coaxial light source, the dome light source, and the ring light source.
According to one aspect of the invention, the backlight source includes a diffuser plate provided as a black translucent material.
According to one aspect of the invention, the surface roughness of the diffusion plate is 3.2 & lt Ra & lt 25, and the absorptivity of the diffusion plate to the light source wave band of the optical system is 70% to Abs & lt 95%.
According to one aspect of the invention, the backlight source further comprises a backlight source lamp bead, the diffusion plate is arranged between the backlight source lamp bead and the light emitting surface of the annular light source, and the distance between the diffusion plate and the backlight source lamp bead is larger than 10 mm.
The invention also provides a detection method using the detection system, which comprises the following steps: and placing a lens between the dome light source and the annular light source, opening the coaxial light source, the dome light source and the annular light source, closing the backlight light source, and shooting a conventional image for defect detection, so that the defect detection method is beneficial to judging whether the defects of surface white spots, insufficient film, dirt and broken filaments exist.
According to an aspect of the invention, the defect detection of the regular image further comprises detecting whether a shallow defect exists:
dividing the conventional image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
According to one aspect of the invention, before the defect detection of the regular image, the ghost elimination of the regular image is further included:
carrying out gray stretching on the conventional image to enable the gray difference value of the lens area to be equal to that of the frosted area, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the conventional image before processing from the transformed image removes ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix.
According to one aspect of the invention, the lens has a diameter of 5mm or less and a thickness of less than 1 mm.
The invention also provides a detection method using the detection system, which comprises the following steps:
placing a lens between the dome light source and the annular light source, turning on the coaxial light source, the dome light source and the annular light source, turning off the backlight light source, and shooting a conventional image;
turning on the backlight light source, turning off the coaxial light source, the dome light source and the annular light source, and shooting a back field image;
the defect detection is carried out on the conventional image, so that the defects of surface white spots, membrane shortage, dirt and broken filaments can be judged;
and detecting the defects of the back field image, and judging whether the black spot defects exist.
According to an aspect of the invention, the defect detection of the regular image and the back field image further comprises detecting whether a shallow defect exists:
dividing the conventional image and the back field image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
According to an aspect of the invention, before the defect detection, the conventional image further comprises ghost elimination:
carrying out gray stretching on the conventional image to enable the gray difference value of the lens area to be equal to that of the frosted area, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the conventional image before processing from the transformed image removes ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix.
According to one aspect of the invention, the lens has a diameter of 5-8mm and a thickness of less than 1 mm.
The invention also provides a detection method using the detection system, which comprises the following steps:
placing a lens between the dome light source and the annular light source, turning on the coaxial light source and the dome light source, turning off the annular light source and the backlight light source, and shooting a bright field image;
turning on the annular light source, turning off the coaxial light source, the dome light source and the backlight light source, and shooting a dark field image;
turning on the backlight light source, turning off the coaxial light source, the dome light source and the annular light source, and shooting a back field image;
the defect detection is carried out on the bright field image, so that the defect of surface white spots, membrane shortage, dirt and white spots can be judged conveniently;
detecting defects of the dark field image, and judging whether defects of scratches, broken filaments and channel subclasses exist or not;
and detecting the defects of the back field image, and judging whether the black spot defects exist.
According to one aspect of the invention, the method further comprises differential image acquisition, which is beneficial to the demoulding defect detection:
subtracting the bright field image and the dark field image by multiplying a set coefficient to obtain a difference image:
Matsub＝a*MatL-b*MatD
wherein: matLFor bright field images, MatDFor dark field images, a and b are scale factors, MatsubIs the differential image.
According to an aspect of the invention, the defect detection of the bright field image, the dark field image and the back field image further comprises detecting whether a shallow defect exists:
dividing the bright field image, the dark field image and the back field image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
According to one aspect of the invention, before the defect detection, ghost elimination is performed on the bright field image and the dark field image:
carrying out gray scale stretching on the bright field image and the dark field image to enable the gray scale difference value of the lens area to be equal to that of the frosted area, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the bright field image and the dark field image before the processing from the transformed images respectively to remove ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix.
According to one aspect of the invention, the lens has a diameter greater than or equal to 8mm and a thickness less than 1 mm.
The invention also provides a detection method using the detection system, which comprises the following steps:
placing a lens with the thickness larger than the depth of field of the telecentric lens between the dome light source and the annular light source, turning on the coaxial light source, the dome light source and the annular light source, and turning off the backlight light source;
the driving device drives the image sensor and the telecentric lens to move for layer shooting, and 2-5 layer shooting images are obtained;
and detecting defects of the obtained images of the layers.
According to one aspect of the invention, the defect detection of the layer shot image comprises shallow defect detection:
dividing each layer shot image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
According to one aspect of the invention, the layer shot image further comprises, before performing defect detection, ghost elimination:
carrying out gray stretching on each layered shot image to enable the gray difference value of the lens area and the frosted area to be equal, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the layer shot image before the unprocessed and the transformed image to remove the ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix.
According to one aspect of the invention, the thickness of the lens is greater than or equal to 1 mm.
The lens defect detection system and the detection method can use different light source combination schemes according to the defect types, and have high detection accuracy and wide application range. The detection method provided by the invention comprises the steps of eliminating ghost interference on the conventional image, the bright field image and the dark field image, enabling the defect exposure to be obvious, improving the detection accuracy and reducing the missing rate. The detection method further comprises the step of obtaining a differential image through the bright field image and the dark field image so as to effectively distinguish white point defects and stripping defects, improve detection capability and reduce omission ratio. The detection method also comprises a shallow defect detection method, and the shallow defect is segmented by analyzing the relative change of the gray value of the local area of the lens, so that the detection sensitivity is improved, and the omission ratio is reduced.
Drawings
FIG. 1 schematically illustrates a schematic structural view of a lens defect detection system according to the present invention;
FIG. 2 schematically illustrates a coaxial light source and dome light source connection according to the present invention;
FIG. 3 schematically shows a combined view of a ring light source and a backlight light source according to the present invention;
FIG. 4 schematically represents a bright field image of a lens;
FIG. 5 schematically represents a dark field image of the lens;
FIG. 6 schematically represents a back field image of a lens;
FIG. 7 schematically represents a conventional image of a lens;
FIG. 8 shows a schematic refraction diagram of an in-line light source;
FIG. 9 is a diagram schematically illustrating a background field image without ghost cancellation;
FIG. 10 is a schematic representation of a diagram after ghost elimination for a back field image;
FIG. 11 is a schematic diagram showing a defect detection method for a lens having a diameter of 5mm or less and a thickness of less than 1 mm;
FIG. 12 is a schematic representation of a shallow defect detection method according to the present invention;
FIG. 13 schematically shows a defect detection method diagram for lenses 5-8mm in diameter and less than 1mm thick;
FIG. 14 is a schematic view showing a defect detection method for a lens having a diameter of 8mm or more and a thickness of less than 1 mm;
FIG. 15 schematically shows imaging in bright field images of white point defects and film release defects;
FIG. 16 schematically represents imaging in a dark field image according to a white point defect;
FIG. 17 shows a differential image obtained from a bright field image and a dark field image;
FIG. 18 is a schematic representation of a method of defect detection for lenses having a thickness greater than the depth of field of a telecentric lens.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
The present invention is described in detail below with reference to the drawings and the specific embodiments, which are not repeated herein, but the embodiments of the present invention are not limited to the following embodiments.
Referring to fig. 1, the present invention provides a lens defect detecting system, which includes a camera unit 2, a coaxial light source 3, a dome light source 4, an annular light source 5 and a backlight light source 6, which are sequentially arranged from top to bottom. In the lens defect detection system, the camera unit 2, the coaxial unit 3, the dome light source 4, the annular light source 5 and the backlight light source 6 are coaxially arranged in sequence, and the dome light source 4 and the annular light source 5 are arranged at intervals. That is, as shown in fig. 1, the dome light source 4 and the ring light source 5 have a space 1 therebetween, and the space 1 is subjected to defect detection due to the placement of the lens. Fig. 1 shows two lens defect inspection systems mounted on the same mounting plate, so that two lenses can be inspected for defects at the same time, thereby improving inspection efficiency.
According to the concept of the invention, the implementation mode of placing the lens to be detected at the interval 1 is not limited, and according to one implementation mode of the invention, a workpiece disc is arranged at the interval 1 between the dome light source 4 and the annular light source 5, and a lens mounting position is arranged on the workpiece disc and used for placing the lens to be detected. According to the second embodiment of the present invention, the lens can be mounted on other tools, and when defect detection is required, the tool is inserted into the space 1 between the dome light source 4 and the annular light source 5, so that the lens is coaxial with the dome light source 4 and the annular light source 5 for detection.
According to one embodiment of the present invention, the image capturing unit 2 of the present invention includes a supporting plate 21, a fixing plate 22 is mounted on the supporting plate 21, and a high-quality image sensor 23 and a high-resolution telecentric lens 24 are sequentially mounted on the fixing plate 22. The camera unit 2 further comprises a driving device 25, for example, the driving device 25 may be configured as a servo motor, and is configured to drive the fixing plate 22 to move, so as to realize the movement of the high-quality image sensor 23 and the high-resolution telecentric lens 24, thereby overcoming the limitation of the depth of field of the lens, so as to take clear pictures of different regions of the lens, and thus being applicable to the detection of the lens with the thickness of the lens larger than the depth of field of the camera.
According to an embodiment of the present invention, the high-quality image sensor 23 of the present invention can provide both hardware configurations of a black-and-white camera and a color camera. Wherein the black and white camera can provide most of the defect detection functions. The color camera adds a detection item of abnormal color defects on the basis of the functions of the black and white camera so as to deal with special defects of partial manufacturers, such as film shortage, abnormal film color and the like.
The coaxial light source 3 and the dome light source 4 are in rigid connection, so that the coaxiality between the light sources can be effectively ensured. Referring to fig. 2, according to one embodiment of the present invention, the dome light source 4 is provided with a connection plate 41, and the coaxial light source 3 is fixedly supported on the connection plate 41 to ensure that the coaxial light source 3 and the dome light source 4 are coaxially arranged.
In addition, the lens defect detection system of the invention has the proportional relation of 2 times or more between the light-emitting surface of the coaxial light source 3 and the light-through hole on the dome light source 4. Such an arrangement may enable the coaxial light source 3 and the dome light source to be in a complementary relationship.
The lens defect detecting system of the present invention is shown in fig. 3, wherein the backlight light source 6 includes a diffusion plate 61. According to one embodiment of the present invention, the color of the diffusion plate 61 is set to be complementary colors (e.g., blue and yellow, red and green, etc.) to the light source band colors of the coaxial light source 3, the dome light source 4, and the ring light source 5, thereby enabling absorption of light energy.
According to the second embodiment of the present invention, the diffusion plate 61 may be made of a black translucent material, so that it is compatible with light source energy of all visible light bands including white light.
In the invention, the color of the diffusion plate 61 and the light source waveband colors of the coaxial light source 3, the dome light source 4 and the annular light source 5 are set to be complementary colors or to be black and translucent, so that the backlight light source 6 is not turned on when the lens is detected, the backlight light source is only used as a background, light rays emitted by the coaxial light source 3, the dome light source 4 and/or the annular light source 5 are absorbed by the diffusion plate and are not reflected into the telecentric lens 24 and the image sensor 23 by the diffusion plate 61, thereby improving the imaging quality and the accuracy of defect detection.
In the invention, Ra is more than or equal to 3.2 and less than or equal to 25, so that the surface of the diffusion plate 61 can be ensured not to reflect light and the concave-convex texture background interference can be avoided. The absorptivity of the diffusion plate 61 to the light source wave band of the optical system is more than or equal to 70% and less than or equal to 95%, so that the backlight source can be used as a dark field background when not being lightened on the premise of ensuring the normal transmission of backlight light. As shown in fig. 3, according to an embodiment of the present invention, the ring-shaped light source 5 and the backlight light source 6 are fixedly connected by screws. The backlight source 6 further comprises backlight source beads, the diffusion plate 61 is used as the light emitting surface of the backlight source 6 and is arranged between the backlight source beads and the light emitting surface of the annular light source 5, and the distance between the diffusion plate 61 and the backlight source beads is larger than 10mm, so that the uniformity of the light emitting surface of the backlight source 6 penetrating through the diffusion plate 61 can reach an ideal value (larger than 90%).
The lens defect detection system is sequentially provided with the coaxial light source 3, the dome light source 4, the annular light source 5 and the backlight light source 6, so that different defect types can be effectively detected by adopting different light source combination lighting modes aiming at lenses with different specifications. In addition, set up camera unit 2 in coaxial light source 3's top to camera unit 2 can move along vertical direction, thereby also can carry out defect detection to the lens that lens thickness is greater than the camera lens depth of field, and application scope is wide.
Specifically, the coaxial light source 3, the dome light source 4, the annular light source 5 and the backlight light source 6 are arranged, so that different lighting combinations can be provided to highlight different defect characteristics during lens detection.
When the coaxial light source 3 and the dome light source 4 are turned on and the ring light source 5 and the backlight light source 6 are turned off, a bright field image can be obtained by the telecentric lens 24. Under the illumination condition, the reflection defects such as surface stripping, surface white spots, film shortage, dirt and the like of the lens are obviously imaged. This is because the transmittance of the defect to light is low, and the transmittance of the non-defect area of the lens is relatively high, so that the imaging effect that the gray value of the defect area is significantly larger than that of the non-defect area of the lens occurs in the image.
When the ring light source 5 is turned on and the coaxial light source 3, dome light source 4 and backlight light source 6 are turned off, a dark field image can be obtained by the telecentric lens 24. Under the illumination condition, defects such as surface white spots, scratches, bubbles, broken filaments, group white spots, technical tracks and the like are obviously imaged. This is because the ring light is diffused and scattered when passing through the surface of such a defect, so that the defect portion is received by the telecentric lens 24, while the light rays in the non-defect area of the lens are directly transmitted and cannot be received by the telecentric lens.
When the backlight source 6 is turned on and the coaxial light source 3, the dome light source 4 and the ring light source 5 are turned off, a back field image can be obtained by the telecentric lens 24. Under the illumination condition, black spot defects are obviously imaged. The reason is that the backlight source directly irradiates the lens, light rays of non-defective parts of the lens can be received by the telecentric lens through the lens, and the direct light source is continuously shielded by the black point defect.
When the coaxial light source 3, the dome light source 4 and the ring light source 5 are turned on, and the backlight light source 6 is turned off, a normal image can be obtained by the telecentric lens. Under the illumination condition, most defects such as surface stripping, surface white spots, film shortage, dirt, broken filaments and the like can be imaged.
Fig. 4-7 schematically show views of a bright field image, a dark field image, a back field image and a conventional image, respectively, from which it can be seen that the corresponding defect features appear evident under different lighting conditions. Therefore, the lens defect detection system can utilize different illumination conditions to combine to obtain one or more images for defect detection, so as to improve the detection precision.
As shown in connection with fig. 8 and 9, the bright field image, dark field image and conventional image of the present invention are ghost images, each including a ghost area and a lens frosted area. Specifically, the coaxial light of the invention is 90 degrees turned by the spectroscope on the coaxial light source, because of the thickness of the spectroscope glass, the light can be reflected on two surfaces of the spectroscope at the same time in the turning process (the prior coating technology can not ensure the 100 percent transmittance of the second reflecting surface), wherein the incident light a and the emergent light b of the second reflecting surface are refracted by the inside of the spectroscope and then emitted from the first reflecting surface, the light c is parallel to but not collinear with the light d directly reflected by the first reflecting surface, and the light finally forms a ghost image after entering the image sensor. The lens defects caused by the interference of ghosting are not obvious enough in performance and are not beneficial to detection, so that ghosting elimination operation is usually required before bright field images, dark field images and conventional images are detected, the ghost eliminated images are obtained as shown in fig. 10, and then defect detection is carried out, and the detection accuracy is favorably improved.
The method for detecting defects of a lens by using the lens defect detection system of the present invention is described in detail below:
the invention provides a lens defect detection method, which is preferably used for detecting defects of a lens with the diameter less than or equal to 5mm and the thickness less than 1 mm. Referring to fig. 11, a lens is placed between the dome light source 4 and the annular light source 5 according to the foregoing embodiment or other embodiments, the coaxial light source 3, the dome light source 4 and the annular light source 5 are turned on, the backlight light source 6 is turned off, and a conventional image is taken for defect detection. Specifically, the image is sequentially subjected to preprocessing, image segmentation and BLOB analysis, and whether the defects of surface white spots, membrane deficiency, dirt and broken filaments exist is judged according to various set defect standard parameters.
Wherein the defect detection of the regular image further comprises detecting whether a shallow defect is present. The shallow defects comprise shallow dirt, shallow scratch, curbs and the like. The specific detection process is shown in fig. 12:
dividing the obtained conventional image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value (set according to actual requirements);
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
The detection method also comprises the following steps of carrying out ghost elimination operation on the conventional image before defect detection:
carrying out gray stretching on the conventional image to enable the gray difference between the lens area and the frosted area to be equal, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the conventional image before processing from the transformed image removes ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix.
The invention also provides a second lens defect detection method. Preferably, the method is used for testing lenses with a diameter of 5-8mm and a thickness of less than 1 mm. As shown in fig. 13, the detection method includes: placing the lens between the dome light source 4 and the annular light source 5 according to the previous embodiment or other embodiments, turning on the coaxial light source 3, the dome light source 4 and the annular light source 5, turning off the backlight light source 6, and taking a conventional image;
turning on a backlight light source 6, turning off a coaxial light source 3, a dome light source 4 and an annular light source 5, and shooting a back field image;
carrying out defect detection on the conventional image, and judging whether surface white spots, membrane shortage, dirt and wool defects exist or not;
and detecting the defects of the back field image, and judging whether the black spot defects exist.
Of course, according to the concept of the present invention, when detecting the defects of the conventional image, if the lens is determined to be defective, the detection process can be terminated, and the result that the lens is a defective lens is output. And the detection of whether the black point defect exists can be continued after the defect is detected in the conventional image, so that the summary statistics of various defect forms of the lens can be carried out, and a basis is provided for the improvement of the production process.
The defect detection method for the lens with the diameter of 5-8mm and the thickness of less than 1mm can also comprise the detection of shallow defects:
dividing the conventional image and the back field image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
In addition, before the conventional image is subjected to defect detection, ghost interference elimination needs to be carried out:
carrying out gray stretching on the conventional image to enable the gray difference between the lens area and the frosted area to be equal, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the conventional image before processing from the transformed image removes ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0As affine transformation matrix。
The invention also provides a third lens defect detection method. Preferably, the detection method is used for detecting the defects of the lens with the diameter of more than or equal to 8mm and the thickness of less than 1 mm. As shown in fig. 14, the detection method includes:
placing the lens between the dome light source 4 and the annular light source 5 according to the previous embodiment or other embodiments, turning on the coaxial light source 3 and the dome light source 4, turning off the annular light source 5 and the backlight light source 6, and taking a bright field image;
turning on an annular light source 5, turning off a coaxial light source 3, a dome light source 4 and a backlight light source 6, and shooting a dark field image;
turning on a backlight light source 6, turning off a coaxial light source 3, a dome light source 4 and an annular light source 5, and shooting a back field image;
detecting defects of the bright field image, and judging whether surface white spots, membrane shortage, dirt and white spot defects exist;
detecting defects of the dark field image, and judging whether defects such as scratches, broken filaments and roads exist or not;
and detecting the defects of the back field image, and judging whether the black spot defects exist.
Similarly, before defect detection is performed on the bright field image and the dark field image, ghost interference needs to be eliminated:
carrying out gray scale stretching on the bright field image and the dark field image to enable the gray scale difference value of the lens area to be equal to that of the frosted area, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the bright field image and the dark field image before the processing from the transformed images respectively to remove ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix.
In addition, for the lens with the diameter being more than or equal to 8mm and the thickness being less than 1mm, the detection of the bright field image, the dark field image and the back field image can also comprise shallow defect detection:
dividing the bright field image, the dark field image and the back field image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
In addition, the defect detection of the lens with the diameter of more than or equal to 8mm and the thickness of less than 1mm also comprises the stripping defect detection to distinguish white point defects from stripping defects:
subtracting the bright field image and the dark field image by multiplying a set coefficient to obtain a difference image:
Matsub＝a*MatL-b*MatD
wherein: matLFor bright field images, MatDFor dark field images, a and b are scale factors, MatsubIs the differential image.
| Type of defect | Bright field image | Dark field image | Back field image |
| White point | Imaging | Imaging | Not imaging |
| Demoulding | Imaging | Not imaging | Not imaging |
TABLE 1
As shown in table 1 and fig. 15 to 17, the principle of obtaining the difference image and detecting the film release defect is as follows: the imaging effect of the white point defect and the stripping defect under different illumination conditions is different. The film release defect is imaged only on bright field images and cannot be distinguished from white spots. It needs to be processed again on the basis of the acquired image. Because the white point is imaged in both the bright field image and the dark field image, after the two images are multiplied by corresponding coefficients and subtracted, the imaging effect of the white point is weakened, and the imaging effect of demoulding is not influenced. Therefore, after the bright field image and the dark field image are obtained, the demoulding defect detection can be carried out.
The invention also provides a method for detecting the defects of the lens with the thickness larger than the depth of field range of the telecentric lens. Referring to FIG. 18, in the present embodiment, the defect detection for the lens with thickness of 1mm or more is performed according to the method:
placing a lens with the thickness of more than or equal to 1mm between the dome light source 4 and the annular light source 5 according to the previous embodiment or other embodiments, turning on the coaxial light source 3, the dome light source 4 and the annular light source 5, and turning off the backlight light source 6;
the driving device 25 drives the image sensor 23 and the telecentric lens 24 to move for layer shooting, so as to obtain 2-5 layer shooting images;
and sequentially carrying out preprocessing, detection area segmentation and BLOB analysis on the obtained images of each layer shot, and judging the defects according to the set defect standard parameters.
According to the inventive concept, the defect detection of the layer shot image may also include shallow defect detection:
dividing each layer shot image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
and calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings as shallow defects if the gray value difference value exceeds a set threshold value.
Likewise, for slice images, a ghost disturbance elimination operation may be performed before defect detection:
carrying out gray stretching on each layer shot image to enable the gray difference value of the lens area and the frosted area to be equal, and then carrying out affine transformation to move to the ghost image area to obtain a transformed image;
subtracting the layer shot image before the unprocessed and the transformed image to remove the ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix.
It should be noted that the above methods of the present invention are not limited to defect detection of lenses with corresponding dimensions, and can also be used to detect defects of lenses with other dimensions. That is, the size of the measurement mentioned herein is an example, and is not limited to the size used, and the measurement accuracy corresponding to the selected measurement mode is different.
The lens defect detection system and the detection method can use different light source combination schemes according to the defect types, and have high detection accuracy and wide application range. The detection method comprises the steps of eliminating ghost interference on the bright field image, the dark field image and the conventional image, enabling defects to be exposed obviously, improving detection accuracy and reducing omission ratio. The detection method further comprises the step of obtaining a differential image through the bright field image and the dark field image so as to effectively distinguish white point defects and stripping defects, improve detection capability and reduce omission ratio. The detection method also comprises a shallow defect detection method, and the shallow defect is segmented by analyzing the relative change of the gray value of the local area of the lens, so that the detection sensitivity is improved, and the omission ratio is reduced.
The lens defect detection system and the detection method of the invention have no limitation on the detection target, and can be an aspheric lens, a spherical lens or a periscopic mobile phone lens and the like.
The above description is only one embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. The lens defect detection system is characterized by comprising a camera unit (2), a coaxial light source (3), a dome light source (4), an annular light source (5) and a backlight light source (6), wherein the camera unit (2), the coaxial light source (3), the dome light source (4), the annular light source (5) and the backlight light source (6) are coaxially arranged, and the dome light source (4) and the annular light source (5) are alternately arranged.
2. Lens defect detection system according to claim 1, characterized in that a connection plate (41) is provided on the dome light source (4), the coaxial light source (3) being fixedly supported on the connection plate (41).
3. Lens defect detection system according to claim 1, characterized in that the light emitting face of the coaxial light source (3) and the clear aperture on the dome light source (4) are in a two-fold and more proportional relationship.
4. Lens defect detection system according to claim 1, characterized in that the camera unit (2) comprises a support plate (21), a fixed plate (22) mounted on the support plate (21), an image sensor (23) coaxially arranged in sequence on the fixed plate (22), a telecentric lens (24) and drive means (25) for driving the fixed plate (22) in movement.
5. Lens defect detection system according to claim 1, characterized in that the backlight light source (6) comprises a diffuser plate (61), the color of the diffuser plate (61) being arranged as a complementary color to the light source band colors of the coaxial light source (3), the dome light source (4) and the ring light source (5) or the diffuser plate (61) being arranged as a black translucent material.
6. The lens defect detecting system of claim 5, wherein the surface roughness of the diffusion plate (61) is Ra ≦ 25 of 3.2, and the absorptivity of the diffusion plate (61) to the optical system light source waveband is Abs ≦ 95% of 70%;
the backlight source (6) further comprises a backlight source lamp bead, the diffusion plate (61) is arranged between the backlight source lamp bead and the light emitting surface of the annular light source (5), and the distance between the diffusion plate and the backlight source lamp bead is larger than 10 mm.
7. An inspection method using the lens defect inspection system of any one of claims 1-6, comprising: placing a lens between the dome light source (4) and the annular light source (5), turning on the coaxial light source (3), the dome light source (4) and the annular light source (5), turning off the backlight light source (6), and shooting a conventional image for defect detection, which is beneficial to judging whether surface white spots, film shortage, dirt and broken filament defects exist;
the defect detection of the regular image further comprises detecting whether a shallow defect exists:
dividing the conventional image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings to be shallow defects if the gray value difference exceeds a set threshold;
before the defect detection is carried out on the conventional image, ghost elimination is carried out on the conventional image:
carrying out gray stretching on the conventional image to enable the gray difference value of the lens area to be equal to that of the frosted area, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the conventional image before processing from the transformed image removes ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix;
the diameter of the lens is less than or equal to 5mm, and the thickness of the lens is less than 1 mm.
8. An inspection method using the lens defect inspection system of any one of claims 1-6, comprising:
placing a lens between the dome light source (4) and the annular light source (5), turning on the coaxial light source (3), the dome light source (4) and the annular light source (5), turning off the backlight light source (6), and taking a conventional image;
turning on the backlight light source (6), turning off the coaxial light source (3), the dome light source (4) and the annular light source (5), and shooting a back field image;
the defect detection is carried out on the conventional image, so that the defects of surface white spots, membrane shortage, dirt and broken filaments can be judged;
detecting the defects of the back field image, and judging whether black point defects exist or not;
the defect detection of the normal image and the back field image further comprises detecting whether a shallow defect exists:
dividing the conventional image and the back field image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings to be shallow defects if the gray value difference exceeds a set threshold;
before defect detection is carried out on the conventional image, ghost elimination is carried out:
carrying out gray stretching on the conventional image to enable the gray difference value of the lens area to be equal to that of the frosted area, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the conventional image before processing from the transformed image removes ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix;
the diameter of the lens is 5-8mm, and the thickness of the lens is less than 1 mm.
9. The inspection method of a lens defect inspection system of any one of claims 1 to 6, comprising:
placing a lens between the dome light source (4) and the annular light source (5), turning on the coaxial light source (3) and the dome light source (4), turning off the annular light source (5) and the backlight light source (6), and taking a bright field image;
turning on the annular light source (5), turning off the coaxial light source (3), the dome light source (4) and the backlight light source (6), and shooting a dark field image;
turning on the backlight light source (6), turning off the coaxial light source (3), the dome light source (4) and the annular light source (5), and shooting a back field image;
the defect detection is carried out on the bright field image, so that the defect of surface white spots, membrane shortage, dirt and white spots can be judged conveniently;
the dark field image is subjected to defect detection, so that whether defects such as scratches, broken filaments and roads exist or not can be judged;
detecting the defects of the back field image, and judging whether black point defects exist or not;
the method also comprises the step of obtaining a differential image, which is beneficial to the demoulding defect detection:
subtracting the bright field image and the dark field image by multiplying a set coefficient to obtain a difference image:
Matsub-a*MatL-b*MatD
wherein: matLFor bright field images, MatDFor dark field images, a and b are scale factors, MatsubThe differential image is solved;
the defect detection of the bright field image, the dark field image and the back field image further comprises the following steps of detecting whether shallow defects exist:
dividing the bright field image, the dark field image and the back field image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings to be shallow defects if the gray value difference exceeds a set threshold;
before defect detection is carried out, ghost elimination is carried out on the bright field image and the dark field image:
carrying out gray scale stretching on the bright field image and the dark field image to enable the gray scale difference value of the lens area to be equal to that of the frosted area, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the bright field image and the dark field image before the processing from the transformed images respectively to remove ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix;
the diameter of the lens is more than or equal to 8mm, and the thickness of the lens is less than 1 mm.
10. The inspection method for a lens defect inspection system according to any of claims 1 to 6, comprising:
placing a lens with a lens thickness larger than the depth of field of the telecentric lens (24) between the dome light source (4) and the annular light source (5), turning on the coaxial light source (3), the dome light source (4) and the annular light source (5), and turning off the backlight light source (6);
the driving device (25) drives the image sensor (23) and the telecentric lens (24) to move for layer shooting, and 2-5 layer shooting images are obtained;
detecting the defects of the obtained layer shot images;
the defect detection of the layer shot image comprises shallow defect detection:
dividing each layer shot image into a plurality of circular rings with stable gray scale change according to the gray scale distribution of the lens;
continuously dividing the divided ring into equally divided rings with the difference value of the inner radius and the outer radius as a set value;
calculating the average gray value of the equally divided circular rings, traversing the pixel values on the equally divided circular rings, and judging the equally divided circular rings to be shallow defects if the gray value difference exceeds a set threshold;
the layer shot image further comprises ghost elimination before defect detection:
carrying out gray level stretching on each layer shot image to enable the gray level difference value of the lens area and the frosted area to be equal, and then carrying out affine transformation to move to a double image area to obtain a transformed image;
subtracting the layer shot image before the unprocessed and the transformed image to remove the ghost interference:
Matres＝Matsrc-a*M0*Matsrc
wherein, MatresFor the resulting image, MatsrcFor ghost images, a is the gray scale stretch coefficient, M0Is an affine transformation matrix;
the thickness of the lens is more than or equal to 1 mm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110158564.8A CN112945988B (en) | 2021-02-04 | 2021-02-04 | Lens defect detection system and detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110158564.8A CN112945988B (en) | 2021-02-04 | 2021-02-04 | Lens defect detection system and detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112945988A true CN112945988A (en) | 2021-06-11 |
| CN112945988B CN112945988B (en) | 2023-11-07 |
Family
ID=76243990
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110158564.8A Active CN112945988B (en) | 2021-02-04 | 2021-02-04 | Lens defect detection system and detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112945988B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487554A (en) * | 2021-06-30 | 2021-10-08 | 深圳市裕展精密科技有限公司 | Imaging device, detection device, and detection method |
| CN113984790A (en) * | 2021-09-28 | 2022-01-28 | 歌尔光学科技有限公司 | Lens quality detection method and device |
| CN114113129A (en) * | 2021-12-03 | 2022-03-01 | 中科计算技术西部研究院 | A system and method for identifying and grasping small defects of a lens |
| CN114113113A (en) * | 2021-11-29 | 2022-03-01 | 哈尔滨工业大学 | Three-light-source microscope system device for positioning and identifying surface micro-defects |
| CN114136987A (en) * | 2021-12-03 | 2022-03-04 | 中科计算技术西部研究院 | Lens deformation defect detection device and method |
| CN115393357A (en) * | 2022-10-28 | 2022-11-25 | 菲特（天津）检测技术有限公司 | Lens surface defect detection method |
| CN115436396A (en) * | 2022-08-31 | 2022-12-06 | 武汉精立电子技术有限公司 | System and method for determining position of foreign matter in display screen |
| CN115791786A (en) * | 2022-08-23 | 2023-03-14 | 深圳思谋信息科技有限公司 | A kind of part defect detection method and related device |
| CN116626069A (en) * | 2023-04-17 | 2023-08-22 | 东莞市瑞图新智科技有限公司 | Lens defect detection method |
| CN118294459A (en) * | 2024-06-06 | 2024-07-05 | 宁德时代新能源科技股份有限公司 | Explosion-proof valve defect detection method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0955532A2 (en) * | 1998-05-07 | 1999-11-10 | Nova C.O.R.D. Ag | Optoelectronic procedure, with defect recognition, for testing the strenght of a structure |
| CN105067639A (en) * | 2015-07-20 | 2015-11-18 | 丹阳市精通眼镜技术创新服务中心有限公司 | Device and method for automatically detecting lens defects through modulation by optical grating |
| CN106248671A (en) * | 2016-03-23 | 2016-12-21 | 上海众思电子设备有限公司 | Many line-scan digital cameras online one side detection device |
| US20170011507A1 (en) * | 2013-10-08 | 2017-01-12 | Emage Vision Pte. Ltd. | System and method for inspection of wet ophthalmic lens |
| CN108956645A (en) * | 2018-07-18 | 2018-12-07 | 丹阳市精通眼镜技术创新服务中心有限公司 | A kind of the optical mirror slip defect detecting device and method of more vision systems |
| CN110361395A (en) * | 2019-08-06 | 2019-10-22 | 天津日博工业技术有限公司 | A kind of waterproof ventilated membrane defect test method and apparatus based on machine vision |
| CN111307421A (en) * | 2020-03-20 | 2020-06-19 | 宁波舜宇仪器有限公司 | Lens defect detection system |
-
2021
- 2021-02-04 CN CN202110158564.8A patent/CN112945988B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0955532A2 (en) * | 1998-05-07 | 1999-11-10 | Nova C.O.R.D. Ag | Optoelectronic procedure, with defect recognition, for testing the strenght of a structure |
| US20170011507A1 (en) * | 2013-10-08 | 2017-01-12 | Emage Vision Pte. Ltd. | System and method for inspection of wet ophthalmic lens |
| CN105067639A (en) * | 2015-07-20 | 2015-11-18 | 丹阳市精通眼镜技术创新服务中心有限公司 | Device and method for automatically detecting lens defects through modulation by optical grating |
| CN106248671A (en) * | 2016-03-23 | 2016-12-21 | 上海众思电子设备有限公司 | Many line-scan digital cameras online one side detection device |
| CN108956645A (en) * | 2018-07-18 | 2018-12-07 | 丹阳市精通眼镜技术创新服务中心有限公司 | A kind of the optical mirror slip defect detecting device and method of more vision systems |
| CN110361395A (en) * | 2019-08-06 | 2019-10-22 | 天津日博工业技术有限公司 | A kind of waterproof ventilated membrane defect test method and apparatus based on machine vision |
| CN111307421A (en) * | 2020-03-20 | 2020-06-19 | 宁波舜宇仪器有限公司 | Lens defect detection system |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487554B (en) * | 2021-06-30 | 2025-04-01 | 富联裕展科技(深圳)有限公司 | Photographing device, detection device and detection method |
| CN113487554A (en) * | 2021-06-30 | 2021-10-08 | 深圳市裕展精密科技有限公司 | Imaging device, detection device, and detection method |
| CN113984790A (en) * | 2021-09-28 | 2022-01-28 | 歌尔光学科技有限公司 | Lens quality detection method and device |
| CN114113113A (en) * | 2021-11-29 | 2022-03-01 | 哈尔滨工业大学 | Three-light-source microscope system device for positioning and identifying surface micro-defects |
| CN114136987B (en) * | 2021-12-03 | 2024-04-30 | 中科计算技术西部研究院 | Lens deformation defect detection device and method |
| CN114113129A (en) * | 2021-12-03 | 2022-03-01 | 中科计算技术西部研究院 | A system and method for identifying and grasping small defects of a lens |
| CN114136987A (en) * | 2021-12-03 | 2022-03-04 | 中科计算技术西部研究院 | Lens deformation defect detection device and method |
| CN114113129B (en) * | 2021-12-03 | 2024-04-30 | 中科计算技术西部研究院 | A system and method for identifying and capturing tiny defects in lenses |
| CN115791786A (en) * | 2022-08-23 | 2023-03-14 | 深圳思谋信息科技有限公司 | A kind of part defect detection method and related device |
| CN115436396A (en) * | 2022-08-31 | 2022-12-06 | 武汉精立电子技术有限公司 | System and method for determining position of foreign matter in display screen |
| CN115393357B (en) * | 2022-10-28 | 2023-01-17 | 菲特（天津）检测技术有限公司 | Lens surface defect detection method |
| CN115393357A (en) * | 2022-10-28 | 2022-11-25 | 菲特（天津）检测技术有限公司 | Lens surface defect detection method |
| CN116626069A (en) * | 2023-04-17 | 2023-08-22 | 东莞市瑞图新智科技有限公司 | Lens defect detection method |
| CN118294459A (en) * | 2024-06-06 | 2024-07-05 | 宁德时代新能源科技股份有限公司 | Explosion-proof valve defect detection method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112945988B (en) | 2023-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112945988A (en) | Lens defect detection system and detection method | |
| US8040502B2 (en) | Optical inspection of flat media using direct image technology | |
| KR100399507B1 (en) | How to recognize and evaluate defects in reflective surface coatings | |
| KR101326455B1 (en) | Apparatus and method for characterizing defects in a transparent substrate | |
| US8532364B2 (en) | Apparatus and method for detecting defects in wafer manufacturing | |
| EP0930498A2 (en) | Inspection apparatus and method for detecting defects | |
| TWI627398B (en) | Epitaxial wafer back-surface inspection device and epitaxial wafer back-surface inspection method using the same | |
| JPH0515978B2 (en) | ||
| CN111579564B (en) | Transparent flat plate defect and diopter change detection system and method | |
| JP3595226B2 (en) | Method and apparatus for detecting edge defect in glass plate | |
| JP2015040835A (en) | Defect inspection device and defect inspection method for transparent tabular body | |
| JP2018025439A (en) | Appearance inspection method and appearance inspection apparatus | |
| KR101741659B1 (en) | Light source apparatus for camera module alien substances inspection | |
| JP3677133B2 (en) | Transparency inspection device | |
| JP4575202B2 (en) | Defect inspection method and defect inspection apparatus for transparent plate-like body | |
| KR101520636B1 (en) | Optical Method and Apparatus of Image Acquisition and Illumination on Irregular Surface | |
| KR20230024646A (en) | An apparatus for detecting defect on surface of a secondary battery and method at the same | |
| JP2012141192A (en) | Method and apparatus for inspecting defect of glass substrate, and method of manufacturing glass substrate | |
| JPH08178855A (en) | Method for inspecting light-transmissive object or specular object | |
| JP5312182B2 (en) | Tire inner surface inspection device | |
| JPH0448251A (en) | Bottle inspecting device | |
| JP2004257776A (en) | Light transmissive body inspection equipment | |
| JPH08178863A (en) | Method for inspecting defect of lenticular lens sheet | |
| JP3149336B2 (en) | Optical member inspection device | |
| JPH0868767A (en) | Apparatus for inspecting flaw of body part of bottle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |