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CN118882520B - A three-dimensional detection device and method for surface defects of large-aperture curved optical elements - Google Patents

A three-dimensional detection device and method for surface defects of large-aperture curved optical elements Download PDF

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CN118882520B
CN118882520B CN202411356826.1A CN202411356826A CN118882520B CN 118882520 B CN118882520 B CN 118882520B CN 202411356826 A CN202411356826 A CN 202411356826A CN 118882520 B CN118882520 B CN 118882520B
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CN118882520A (en
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侯溪
马梦聪
胡小川
周杨
杨玥
李梦凡
辛强
范斌
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Institute of Optics and Electronics of CAS
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
    • G01M11/02—Testing optical properties
    • G01M11/0242—Testing optical properties by measuring geometrical properties or aberrations
    • G01M11/0278—Detecting defects of the object to be tested, e.g. scratches or dust
    • 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
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • 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
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    • 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
    • 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
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N2021/0106—General arrangement of respective parts
    • G01N2021/0112—Apparatus in one mechanical, optical or electronic block
    • 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
    • G01N2021/9511—Optical elements other than lenses, e.g. mirrors

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Abstract

The invention relates to a three-dimensional detection device and method for surface defects of a large-caliber curved surface optical element, which belong to the field of three-dimensional detection of surface defects of optical elements, wherein the device comprises a mobile platform, a 3D line spectrum confocal sensor, a calibration head, an industrial camera, a telecentric lens, an annular light source, a point light source, a camera axis fine adjustment device and the like; the method comprises the steps of positioning and focusing the element to be detected, scanning by a 3D line spectrum confocal sensor to obtain a defect position, obtaining defect three-dimensional information, switching to a two-dimensional defect detection module to obtain surface defect two-dimensional information, and comprehensively analyzing the two-dimensional information and the three-dimensional information to obtain a three-dimensional detection result of each defect on the surface of the element to be detected. According to the invention, two defect detection modules are carried on the mobile platform, so that the rapid positioning and high-precision three-dimensional detection of the surface defects of the large-caliber curved surface optical element are realized.

Description

Three-dimensional detection device and method for surface defects of large-caliber curved surface optical element
Technical Field
The invention relates to the field of three-dimensional detection of surface defects of optical elements, in particular to a three-dimensional detection device and method of surface defects of a large-caliber curved surface optical element.
Background
With the continuous development of advanced optical technology, the large-caliber curved surface optical element is gradually applied to the fields of astronomy, space optics and the like. But the surface defects generated in the manufacturing and transportation processes of the large-caliber curved surface optical element can reduce the quality of light beams and influence the normal operation of a system, so that the method has important significance for high-precision three-dimensional defect detection on the surface of the large-caliber curved surface optical element.
Currently, scanning probe type surface profilers, white light interferometers, structured light detection methods and three-dimensional point cloud measurement technologies are mainly used for three-dimensional detection of surface defects of curved optical elements. The scanning probe type surface profiler is used for contact detection, and secondary damage is easily caused to the element. When the white light interferometer is used for detecting curved surface defects, the angle of the white light interferometer needs to be adjusted, and the operation is complicated. Although the sensitivity of the structured light three-dimensional detection is high, the calculation is complex. The non-contact three-dimensional point cloud measurement technology has the advantages of high precision and large detection range, the acquired point cloud data contains a large amount of detail information, and the defect length, width and depth information can be acquired while the morphology of the element is detected, so that the method is gradually applied to defect detection of the optical element. In the application of CN 110404816A, which is named as a device and a method for detecting defects of 3D curved glass based on a mechanical arm, curved glass is adsorbed and moved by the mechanical arm, and element surface images under different light sources are acquired by utilizing two line scanning cameras so as to detect the defects. In the application of the publication No. CN117619759A, the invention discloses a visual inspection system and method for industrial assembly objects, which is characterized in that an object is grabbed by a mechanical arm, and a multi-angle image combination acquisition mode is adopted by a plurality of industrial cameras to judge and identify the appearance defects of elements. In the three-dimensional defect detection of the above elements, the form that the mechanical arm control element moves and the imaging system is fixed is adopted, but the mechanical arm control element is used for detecting the defects of the small-caliber curved surface optical element, the omnibearing scanning of the optical element is difficult to ensure, and the mechanical arm control element is used for detecting the defects of the large-caliber curved surface optical surface with high precision and acquiring the three-dimensional information of the defects, and further improvement is needed.
Disclosure of Invention
The invention aims to overcome the defects of the existing three-dimensional defect detection technology and provides a three-dimensional defect detection device and method for a large-caliber curved surface optical element. The device is provided with a 3D line spectrum confocal sensor and a camera lens module through a mobile platform, and depth and defect position information of defects are obtained on the basis of point cloud data obtained by the 3D line spectrum confocal sensor. And switching to an imaging module of the camera lens according to the defect position information to acquire the two-dimensional information of the defect. The two modes are combined to finish three-dimensional high-precision detection of the surface defects of the curved optical element.
The three-dimensional detection device for the surface defects of the large-caliber curved surface optical element comprises a moving platform, a 3D line spectrum confocal sensor, an industrial camera, a telecentric lens, an annular light source, a point light source and a camera shaft fine adjustment device, wherein the 3D line spectrum confocal sensor forms a three-dimensional point cloud detection module, the industrial camera, the telecentric lens, the annular light source, the point light source and the camera shaft fine adjustment device form a two-dimensional defect detection module, the two-dimensional defect detection module is closely attached to the front end of the three-dimensional point cloud detection module, the three-dimensional point cloud detection module and the two-dimensional defect detection module are carried on the moving platform, the industrial camera is located at the uppermost end of the two-dimensional defect detection module to achieve an image acquisition function, the telecentric lens is installed at the right lower end of the industrial camera to achieve clear imaging, the annular light source is installed at the lower end of the telecentric lens to achieve dark field illumination, the point light source is installed at an opening of the side face of the telecentric lens to achieve bright field illumination, the camera shaft fine adjustment device is located in the middle of the 3D line spectrum confocal sensor and the telecentric lens to control the two-dimensional defect detection module to conduct up-down fine adjustment to achieve image focusing, the three-dimensional point cloud detection module is used for obtaining the two-dimensional defect detection module, the three-dimensional point cloud detection module is used for obtaining the data of the large-caliber curved surface optical element.
The invention also provides a three-dimensional detection method for the surface defects of the large-caliber curved surface optical element, which comprises the following steps:
The method comprises the steps of S1, positioning and focusing an element to be measured, namely, placing the element to be measured on a sample placing platform, obtaining space coordinates of different positions of the element to be measured by utilizing a calibration head at the lower end of a 3D line spectrum confocal sensor, and completing positioning of the element to be measured;
The method comprises the steps of S2, scanning a 3D line spectrum confocal sensor to obtain a defect position and three-dimensional information of the defect, wherein the scanning path of the 3D line spectrum confocal sensor and the moving speed of a moving platform are determined according to the curvature radius, caliber and space position of an element to be detected, the element to be detected is scanned according to a set path, the obtained point cloud data is stored, and the defect position and the three-dimensional information are determined after analysis and calculation of the point cloud data;
the method comprises the steps of S3, switching to a two-dimensional defect detection module to obtain two-dimensional information of the defect, moving the two-dimensional defect detection module to a defect according to the offset between the two-dimensional defect detection module and the three-dimensional point cloud detection module, focusing clearly, and obtaining bright field or dark field images of the defect;
And S4, comprehensively analyzing the two-dimensional information and the three-dimensional information to obtain a three-dimensional detection result of each defect on the surface of the element to be detected.
Compared with the prior art, the invention has the following beneficial effects:
According to the invention, the three-dimensional point cloud data and the two-dimensional machine vision image are respectively obtained through the 3D line spectrum confocal sensor module and the camera lens two-dimensional detection module, so that the high-precision three-dimensional detection and quantitative evaluation of the surface defects of the curved optical element are realized.
According to the defect position obtained by the three-dimensional point cloud data, the scanning path of the two-dimensional defect detection module can be planned in a self-defined mode, and rapid and accurate detection is achieved.
The 3D line spectrum confocal sensor module and the two-dimensional detection module can be mounted on various large-scale moving platforms, such as a mechanical arm, a portal frame and the like, through a quick switching device, and can be used for detecting large-caliber curved surface elements with calibers of 1-2 m.
The 3D line spectrum confocal sensor module and the two-dimensional detection module can be used independently and are used for in-situ detection and off-line detection of surface defects of the curved optical element.
Drawings
Fig. 1 is a schematic diagram of a device with a mechanical arm as a mobile platform according to the present invention.
Fig. 2 is a schematic diagram of an apparatus of the present invention in which the moving platform is a gantry.
Fig. 3 is a flow chart of the method proposed by the present invention.
Fig. 4 is a point cloud corresponding to a defect on the surface of an optical element in an embodiment.
Fig. 5 is a defect extracted after the point cloud image processing in the embodiment.
Fig. 6 is a dark field scatter image of the same defect taken under a ring light source in an embodiment.
Fig. 7 is a complete scratch map of a mosaic of dark field scatter images.
Fig. 8 is a minimum bounding rectangle corresponding to a scratch in a dark field.
Fig. 9 is a bright field scattering image taken under a point light source for the same defect in the embodiment.
Fig. 10 is a full scratch map of a mosaic of multiple Zhang Mingchang scatter images.
Fig. 11 is a minimum bounding rectangle corresponding to a scratch in bright field.
Reference numerals illustrate:
1. The system comprises a movable platform, a 2.3D line spectrum confocal sensor, a camera shaft fine adjustment device, an industrial camera, a 5 telecentric lens, a6 point light source, a 7 sensor probe, an 8 annular light source, a 9 calibration head, a 10 element to be measured and a 11 sample placement platform.
Detailed Description
The present invention will be described in detail with reference to the drawings and examples, and it should be understood that the scope of the invention is not limited to the examples.
The invention provides a three-dimensional detection device for surface defects of a large-caliber curved surface optical element, which is shown in fig. 1 and 2. The device comprises a mobile platform 1, a 3D line spectrum confocal sensor 2, a camera shaft fine-tuning device 3, an industrial camera 4, a high-resolution telecentric lens 5, a point light source 6, a sensor probe 7, an annular light source 8, a calibration head 9, a component to be tested 10 and a sample placing platform 11. In fig. 1, the moving platform 1 adopts a mechanical arm, and in fig. 2, the moving platform 1 adopts a portal frame. The 3D line spectrum confocal sensor 2 is a main component of a three-dimensional point cloud detection module, the industrial camera 4, the telecentric lens 5, the annular light source 8, the point light source 6 and the camera axis fine adjustment device 3 are components of a two-dimensional defect detection module, and the two-dimensional defect detection module is closely attached to the front end of the three-dimensional point cloud detection module. The sensor probe 7 is positioned right below the 3D line spectrum confocal sensor 2, the sensor probe 7 is used for emitting line light and receiving the line light reflected by the element to be detected 10, the calibration head 9 is arranged below the 3D line spectrum confocal sensor 2 and positioned between the sensor probe 7 and the two-dimensional defect detection module, the position coordinates of three points on the boundary of the element to be detected 10 are determined through the calibration head 9, the space position of the element to be detected 10 is further determined, the industrial camera 4 is positioned at the uppermost end of the two-dimensional defect detection module to realize an image acquisition function, the telecentric lens 5 is arranged at the right lower end of the industrial camera 4 to realize clear imaging, the annular light source 8 is arranged at the lower end of the telecentric lens 5 to realize dark field illumination, the point light source 6 is arranged at an opening of the side face of the telecentric lens to realize bright field illumination, and the camera axis fine adjustment device 3 is positioned between the 3D line spectrum confocal sensor 2 and the telecentric lens 5 to control the two-dimensional defect detection module to conduct up-down fine adjustment to realize image focusing.
The mobile platform 1 drives the 3D line spectrum confocal sensor 2 to scan the large-caliber curved surface optical element according to a set path, and point cloud data of the whole surface of the element is obtained.
The two-dimensional defect detection module can select the point light source 6 or the annular light source 8 for illumination, namely, a group of equipment can realize two imaging modes of bright field and dark field. The point light source 6 is refracted by the lens opening to realize bright field illumination and obtain a machine vision image of bright background and dark defect, and the annular light source 8 is positioned below the lens to realize dark field illumination and obtain a machine vision image of dark background and bright defect. The two images are combined and analyzed, so that more accurate two-dimensional information of the defect can be obtained.
The two-dimensional defect detection module can obtain clear defect images in a rough adjustment and fine adjustment combined mode. Coarse adjustment, namely controlling the mobile platform to move 1 to the defect occurrence in the picture. The fine tuning, i.e. the movement of the module is controlled by the camera axis fine tuning device 3, so that the defect image is clear.
The invention provides a three-dimensional detection method for surface defects of a large-caliber curved surface optical element, which adopts the three-dimensional detection device for surface defects of the large-caliber curved surface optical element, and the implementation flow is shown in figure 3. The method specifically comprises the following steps:
Step S1, positioning and focusing the element to be measured, namely, placing the element to be measured 10 on a sample placing platform 11, carrying a 3D line spectrum confocal sensor 2 on a moving platform 1, and positioning by using a calibration head 9. And (3) moving the 3D line spectrum confocal sensor 2 downwards to the clear position of the light spot to finish focusing.
And S2, scanning the 3D line spectrum confocal sensor 2 to obtain a defect position and defect three-dimensional information, wherein the scanning path of the 3D line spectrum confocal sensor 2 is determined according to the position, caliber and curvature radius of the element to be detected. And processing the scanned acquired point cloud data, and determining the position of each defect and the depth information of the defect. The point cloud data processing method comprises the following steps:
The step S2.1 of fitting a plane by using point cloud data comprises the steps of performing plane fitting by using a RANSAC algorithm, wherein a plane fitting formula and an equation coefficient solving formula are as follows:
(1)
Wherein A, B, C, D is the coefficient of the plane equation, For the coordinates of any three points in the point cloud data,The coordinates of the points in the point cloud data.
Step S2.2 determines the defect location comprising calculating the distance of each point to the fitting plane. If the distance is greater than a set distance threshold T 1, the point is considered as a defect point, and then the positions of the points on the defect are determined;
(2)
Where i is the i-th point in the point cloud dataset, For the coordinates of the point(s),The distance from the point to the fitting plane.
And S2.3, point cloud clustering, namely distinguishing different defects by adopting an European clustering method. And calculating Euclidean distance between a defect point and surrounding k adjacent points. If the Euclidean distance is smaller than the Euclidean distance threshold T 2, placing the adjacent points into the class aggregation of the defect;
(3)
Wherein q is Euclidean distance between the defect point and a certain adjacent point, Is the position coordinates of the defect point,Is the position coordinates of a certain neighboring point.
And S2.4, obtaining three-dimensional defect information, wherein the three-dimensional defect information comprises determining the difference value of the maximum value and the minimum value of coordinates of defect points in the same class set in all directions to determine the depth, the length, the width and other information of the defects.
And S3, switching to a two-dimensional defect detection module to acquire surface defect two-dimensional information, specifically switching to a two-dimensional defect detection module consisting of an industrial camera 4 and a high-resolution telecentric lens 5 according to the position of the surface defect of the element, selecting a point light source 6 or an annular light source 8 for illumination, and focusing by using a camera axis fine adjustment device 3 to acquire a bright field or dark field defect image. The method comprises the steps of splicing a plurality of acquired bright field images or dark field images by adopting a SIFT splicing algorithm, and carrying out image processing on spliced bright field or dark field defect images, wherein the method specifically comprises the following steps:
Step S3.1, constructing a Gaussian scale space, which comprises the steps of convolving an image with Gaussian kernels with different scale coefficients:
(4)
Wherein, As a gaussian scale space function of a certain scale,Is a gaussian function, sigma is a gaussian scale factor,For the defect image to be splicedGray values at coordinates.
Step S3.2, constructing a differential Gaussian scale space, which comprises the steps of subtracting adjacent Gaussian functions and then convolving:
(5)
Wherein, As a gaussian function of variable dimensions,K is a scale factor between adjacent layers as a Gaussian differential scale space function.
And step S3.3, determining the main direction of the characteristic point, wherein the step includes that if a certain sampling point in the Gaussian differential pyramid is an extreme point in the neighborhood of 3 multiplied by 3, the extreme point is the characteristic point. And calculating the gradient amplitude and the gradient direction in the 3 sigma neighborhood range with the characteristic point as the center. Counting gradient distribution of pixels in the neighborhood by using the histogram, wherein the direction with the maximum gradient amplitude is the main direction of the feature point;
(6)
Wherein, Is positioned atThe gradient magnitude of the feature point at the coordinates,Is positioned atGradient direction of feature points at coordinates.Is positioned atThe gaussian scale space value of the point at the coordinates,Is positioned atThe gaussian scale space value of the point at the coordinates,Is positioned atThe gaussian scale space value of the point at the coordinates,Is positioned atThe gaussian scale space value of the point at the coordinates.
Step S3.4 of generating feature point descriptors comprises calculating gradient information of 8 directions in 4×4 regions with the feature points as the center to obtain 128-dimensional feature point descriptors.
And step S3.5, matching the feature points and splicing the images, wherein the step S comprises the step of calculating the distances among all feature point descriptors on different images, and the feature point pairs with the distances smaller than a distance threshold T 3 are the matching point pairs. And calculating a transformation matrix of the image according to the spatial positions of the matching point pairs, and completing the image splicing.
Step S3.6, filtering the spliced image, which comprises the steps of smoothing and denoising the spliced image by using Gaussian filter, wherein the Gaussian filter function is as follows:
(7)
Wherein G (x, y) is a Gaussian filter function, Is the position coordinates.
Step S3.6, background non-uniformity correction, which comprises the following steps of performing top-hat transformation processing on the filtered dark field image to avoid the influence of non-uniform illumination:
(8)
Wherein I 1 is a dark field stitched image after correction illumination, f 1 is a dark field stitched image before correction, b is a structural element, Represents an on operation in morphological processing.
And performing bottom cap transformation processing on the filtered bright field spliced image:
(9)
Wherein I 2 is a bright field stitched image after correction of illumination, f 2 is a bright field stitched image before correction, b is a structural element.
S3.7, binarizing the image;
and respectively determining a binarization threshold T 4 of the dark field or bright field spliced image after the illumination correction by using a maximum inter-class variance method. The pixel gray value of which the gray value is larger than the binarization threshold value T 4 in the image is set to be 1, and the pixel gray value of which the gray value is smaller than the binarization threshold value T 4 is set to be 0.
S3.8, obtaining defect two-dimensional information;
and processing the dark field spliced image and the bright field spliced image by using eight connected domains respectively, wherein the pixels in the same connected domain form a defect. And selecting the defect by using a minimum circumscribed rectangle, wherein the length of the minimum circumscribed rectangle is the length of the defect, and the width of the minimum circumscribed rectangle is the width of the defect.
And S4, comprehensively analyzing to obtain a defect three-dimensional detection result, wherein the depth obtained by the point cloud data processing result is taken as the defect depth. Since the image processing methods adopted in the bright field and the dark field are slightly different and have certain deviation, the average value of the length and the width of the two is selected as the length and the width of the scratch.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
First, two defect inspection modules are fixed on a moving stage, and the device under test 10 is placed on a sample placement stage 11.
And (3) starting detection, planning a scanning path according to the position, caliber and curvature radius of the element to be detected 10, and completing scanning to obtain point cloud data. Wherein the point cloud at a defect is shown in fig. 4. After plane fitting and clustering of the point cloud image, defect information can be extracted, as shown in fig. 5, namely, defect length 8.228mm, width 0.148mm and maximum depth 10.900um.
And detecting by using a two-dimensional defect detection module consisting of the industrial camera 4 and the high-magnification telecentric lens 5 according to the position of the surface defect of the element, and acquiring two-dimensional information of the defect after splicing, filtering, background correction, binarization and connected domain determination of the acquired scattering image. The method comprises the steps of obtaining dark field defect images obtained when an annular light source is used, as shown in fig. 6, obtaining spliced dark field scattering images, as shown in fig. 7, obtaining minimum circumscribed rectangular images corresponding to scratches in a dark field, as shown in fig. 8, obtaining bright field defect images obtained when a point light source is used, as shown in fig. 9, obtaining spliced bright field scattering images, as shown in fig. 10, and obtaining minimum circumscribed rectangular images corresponding to scratches in a bright field, as shown in fig. 11. For this example, the scratch length obtained in the dark was 8.280mm, the width was 0.197mm, the scratch length obtained in the light was 8.295mm, and the width was 0.197mm. Because the treatment methods adopted in the bright field and the dark field are different, certain deviation exists, the average value of the two is selected as the length and the width of the scratch, namely the length of the scratch is 8.288mm, and the width is 0.197mm.
And finally, comprehensively analyzing the obtained two-dimensional defect information and three-dimensional defect information of the sample. In the two-dimensional data processing process, the width of the defect is obtained by using the minimum circumscribed rectangle, and the width is enlarged, so that the width and the depth of the defect can be obtained after three-dimensional point cloud data processing, and the length can be obtained after two-dimensional image processing. I.e. the scratch defect in the example has a length 8.288mm, a width of 0.148mm and a maximum depth of 10.900um. Therefore, the three-dimensional detection device and the method for the surface defects of the large-caliber curved surface optical element are realized.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereto, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.

Claims (4)

1.一种大口径曲面光学元件表面缺陷三维检测方法，所述方法基于一种大口径曲面光学元件表面缺陷三维检测装置实现，所述装置包括移动平台、3D线光谱共焦传感器、工业相机、远心镜头、环形光源、点光源、相机轴微调装置；其中，3D线光谱共焦传感器构成三维点云检测模块；工业相机、远心镜头、环形光源、点光源、相机轴微调装置构成二维缺陷检测模块；二维缺陷检测模块紧靠于三维点云检测模块前端；三维点云检测模块和二维缺陷检测模块搭载至移动平台；其特征在于，所述方法包括步骤如下：1. A three-dimensional detection method for surface defects of large-aperture curved optical elements, the method is implemented based on a three-dimensional detection device for surface defects of large-aperture curved optical elements, the device comprising a mobile platform, a 3D line spectrum confocal sensor, an industrial camera, a telecentric lens, an annular light source, a point light source, and a camera axis fine-tuning device; wherein the 3D line spectrum confocal sensor constitutes a three-dimensional point cloud detection module; the industrial camera, the telecentric lens, the annular light source, the point light source, and the camera axis fine-tuning device constitute a two-dimensional defect detection module; the two-dimensional defect detection module is close to the front end of the three-dimensional point cloud detection module; the three-dimensional point cloud detection module and the two-dimensional defect detection module are mounted on the mobile platform; the method comprises the following steps: 步骤S1：待测元件定位与对焦；包括：将待测元件放置于样品放置平台上，利用3D线光谱共焦传感器下端的定标头获取待测元件不同位置处的空间坐标，完成待测元件的定位；下移3D线光谱共焦传感器至光斑清晰位置，完成对焦；Step S1: Positioning and focusing the component to be tested; including: placing the component to be tested on the sample placement platform, using the calibration head at the lower end of the 3D line spectrum confocal sensor to obtain the spatial coordinates of different positions of the component to be tested, and completing the positioning of the component to be tested; moving the 3D line spectrum confocal sensor downward to a position where the light spot is clear, and completing the focusing; 步骤S2：3D线光谱共焦传感器扫描获取缺陷位置，获取缺陷三维信息；包括：根据待测元件的曲率半径、口径和空间位置，确定3D线光谱共焦传感器的扫描路径和移动平台的移动速度，按照既定路径扫描待测元件，保存获取的点云数据，对点云数据分析计算后确定缺陷位置及三维信息；Step S2: Scanning the 3D line spectrum confocal sensor to obtain the defect position and the three-dimensional information of the defect; including: determining the scanning path of the 3D line spectrum confocal sensor and the moving speed of the mobile platform according to the curvature radius, caliber and spatial position of the component to be tested, scanning the component to be tested according to the predetermined path, saving the obtained point cloud data, and determining the defect position and three-dimensional information after analyzing and calculating the point cloud data; 步骤S3：切换至二维缺陷检测模块，获取缺陷二维信息；包括：针对某一已确定位置的缺陷，结合根据二维缺陷检测模块和三维点云检测模块之间的偏移量，将二维缺陷检测模块移动至缺陷处，对焦清晰后并获取缺陷的明场或暗场图像；若缺陷处于多个视场内，根据缺陷位置自定义扫描路径，完成整个缺陷的明场或暗场图像的拼接以及图像处理后，获取缺陷的二维信息；Step S3: Switch to the two-dimensional defect detection module to obtain two-dimensional information of the defect; including: for a defect at a certain determined position, according to the offset between the two-dimensional defect detection module and the three-dimensional point cloud detection module, move the two-dimensional defect detection module to the defect, focus clearly and obtain a bright field or dark field image of the defect; if the defect is in multiple fields of view, customize the scanning path according to the defect position, complete the splicing of the bright field or dark field image of the entire defect and image processing, and then obtain the two-dimensional information of the defect; 步骤S4：将二维信息与三维信息进行综合分析，获取待测元件表面上各缺陷的三维检测结果。Step S4: Comprehensively analyze the two-dimensional information and the three-dimensional information to obtain three-dimensional detection results of each defect on the surface of the component to be tested. 2.根据权利要求1所述的一种大口径曲面光学元件表面缺陷三维检测方法，其特征在于，步骤S2中，点云数据分析计算的具体步骤如下：2. A method for three-dimensional detection of surface defects of large-aperture curved optical elements according to claim 1, characterized in that in step S2, the specific steps of point cloud data analysis and calculation are as follows: 步骤S2.1：利用点云数据拟合平面；包括：Step S2.1: Fitting a plane using point cloud data; including: 进行平面拟合，平面拟合公式和平面方程系数求解公式如下：Perform plane fitting, the plane fitting formula and the plane equation coefficient solution formula are as follows: （1） (1) 其中，A、B、C、D为平面方程的系数，为点云数据中的任意三个点的坐标； 为点云数据中点的坐标；Among them, A, B, C, and D are the coefficients of the plane equation. are the coordinates of any three points in the point cloud data; is the coordinate of the point in the point cloud data; 步骤S2.2：确定缺陷位置；包括：计算每个点到拟合平面的距离；若该距离大于设定的距离阈值T1，则认为该点为缺陷上的点，进而确定缺陷上各点的位置；Step S2.2: Determine the defect position; including: calculate the distance from each point to the fitting plane; if the distance is greater than the set distance threshold T 1 , the point is considered to be a point on the defect, and then determine the position of each point on the defect; （2） (2) 其中，i为点云数据集中的第i个点，为该点坐标，为该点到拟合平面的距离；Among them, i is the i-th point in the point cloud dataset, is the coordinate of the point, is the distance from the point to the fitting plane; 步骤S2.3：点云类聚；包括：采用欧式聚类法对不同缺陷进行区分；具体为：计算缺陷上的一个点与周围k个邻近点的欧氏距离，若欧式距离小于欧式距离阈值T2，则将邻近点放入该缺陷的类聚集合中；Step S2.3: point cloud clustering; including: using Euclidean clustering to distinguish different defects; specifically: calculating the Euclidean distance between a point on the defect and its k neighboring points; if the Euclidean distance is less than the Euclidean distance threshold T 2 , the neighboring point is put into the clustering of the defect; （3） (3) 其中，q为缺陷上的点与某一邻近点的欧氏距离，为缺陷上的点的位置坐标，为某一个邻近点的位置坐标；Where q is the Euclidean distance between a point on the defect and a neighboring point, is the position coordinate of the point on the defect, is the position coordinate of a nearby point; 步骤S2.4：获取缺陷上的点的三维信息；包括：对同一个类聚集合，确定类内缺陷上的点在各个方向上坐标的最大值和最小值的差值来确定缺陷的深度、长度和宽度信息。Step S2.4: Obtain three-dimensional information of points on the defect; including: for the same cluster, determine the difference between the maximum and minimum coordinates of the points on the defect in each direction within the cluster to determine the depth, length and width information of the defect. 3.根据权利要求1所述的一种大口径曲面光学元件表面缺陷三维检测方法，其特征在于，步骤S3中，缺陷的明场或暗场图像拼接以及图像处理的具体步骤如下：3. A method for three-dimensional detection of surface defects of large-aperture curved optical elements according to claim 1, characterized in that in step S3, the specific steps of bright field or dark field image stitching and image processing of the defects are as follows: 步骤S3.1：构建高斯尺度空间；包括：将图像与不同尺度系数的高斯核进行卷积：Step S3.1: Constructing a Gaussian scale space; including: convolving the image with Gaussian kernels of different scale coefficients: （4） (4) 其中，为某一尺度的高斯尺度空间函数，为高斯函数，σ 为待拼接缺陷图像在坐标处的灰度值；in, is a Gaussian scale space function of a certain scale, is a Gaussian function, σ The defect image to be stitched is Gray value at coordinates; 步骤S3.2：构建差分高斯尺度空间；包括：对相邻高斯函数进行相减后再卷积：Step S3.2: constructing a differential Gaussian scale space; including: subtracting adjacent Gaussian functions and then convolving them: （5） (5) 其中，为尺度可变的高斯函数，为高斯差分尺度空间函数，k为相邻层数间的比例因子；in, is a Gaussian function with variable scale, is the Gaussian difference scale space function, k is the scaling factor between adjacent layers; 步骤S3.3：确定特征点的主方向；包括：若高斯差分金字塔中某一采样点为其邻域内的极值点，则该极值点为特征点，计算以特征点为中心的3σ邻域范围内的梯度幅值和梯度方向，利用直方图统计邻域内像素的梯度分布，梯度幅值最大的方向即特征点的主方向；Step S3.3: determining the main direction of the feature point; including: if a sampling point in the Gaussian difference pyramid is an extreme point in its neighborhood, then the extreme point is the feature point, calculating the gradient amplitude and gradient direction within the 3σ neighborhood range centered on the feature point, using a histogram to count the gradient distribution of pixels in the neighborhood, and the direction with the largest gradient amplitude is the main direction of the feature point; （6） (6) 其中，为位于坐标处特征点的梯度幅值，为位于坐标处特征点的梯度方向，为位于坐标处的点的高斯尺度空间值，为位于坐标处的点的高斯尺度空间值，为位于坐标处的点的高斯尺度空间值，为位于坐标处的点的高斯尺度空间值；in, For The gradient amplitude of the feature point at the coordinate, For The gradient direction of the feature point at the coordinate, For The Gaussian scale-space value of the point at coordinates, For The Gaussian scale-space value of the point at coordinates, For The Gaussian scale-space value of the point at coordinates, For Gaussian scale-space value of the point at coordinates; 步骤S3.4：生成特征点描述符；包括：以特征点为中心，计算在多个区域内多个方向的梯度信息，获得多维的特征点描述符；Step S3.4: generating a feature point descriptor; including: taking the feature point as the center, calculating the gradient information in multiple directions in multiple regions to obtain a multi-dimensional feature point descriptor; 步骤S3.5：特征点匹配与图像拼接；包括：计算不同图像上所有特征点描述符之间的距离，距离小于距离阈值T3的特征点对则为匹配点对，根据各匹配点对的空间位置计算图像的变换矩阵，完成图像的拼接得到暗场拼接图像或明场拼接图像；Step S3.5: feature point matching and image stitching; including: calculating the distances between all feature point descriptors on different images, and the feature point pairs whose distances are less than the distance threshold T 3 are matching point pairs, calculating the image transformation matrix according to the spatial position of each matching point pair, and completing the image stitching to obtain a dark field stitching image or a bright field stitching image; 步骤S3.6：对暗场拼接图像或明场拼接图像进行滤波处理；包括：使用高斯滤波器对图像进行平滑去噪，高斯滤波器函数如下：Step S3.6: filtering the dark field stitching image or the bright field stitching image; including: using a Gaussian filter to smooth and remove noise from the image, the Gaussian filter function is as follows: （7） (7) 其中，G(x,y) 为高斯滤波函数， (x,y)为位置坐标；Among them, G(x,y) is the Gaussian filter function, (x,y) is the position coordinate; 步骤S3.7：背景不均匀校正；包括：对滤波后的暗场拼接图像进行顶帽变换处理：Step S3.7: Background unevenness correction; including: performing top-hat transformation on the filtered dark field stitching image: （8） (8) 其中，I1为校正光照后的暗场拼接图像，f1为校正前的暗场拼接图像，b为结构元素，表示形态学处理中的开操作；Among them, I 1 is the dark field stitching image after correction of illumination, f 1 is the dark field stitching image before correction, b is the structural element, Represents the opening operation in morphological processing; 对于滤波后的明场拼接图像进行底帽变换处理：Perform bottom hat transformation on the filtered bright field stitching image: （9） (9) 其中，I2为校正光照后的明场拼接图像，f2为校正前的明场拼接图像，b为结构元素，•表示形态学处理中的闭操作；Among them, I 2 is the bright field stitching image after correction of illumination, f 2 is the bright field stitching image before correction, b is the structural element, and • represents the closing operation in morphological processing; 步骤S3.8：图像二值化；包括：使用最大类间方差法分别确定校正光照后的暗场拼接图像或者明场拼接图像的二值化阈值T4，将图像中灰度值大于二值化阈值T4的像素点灰度值设为1，灰度值小于阈值二值化阈值T4的像素点灰度值设为0；Step S3.8: image binarization; including: using the maximum inter-class variance method to determine the binarization threshold T 4 of the dark field stitching image or the bright field stitching image after the illumination correction, respectively, setting the gray value of the pixel point with a gray value greater than the binarization threshold T 4 in the image to 1, and setting the gray value of the pixel point with a gray value less than the threshold binarization threshold T 4 to 0; 步骤S3.9：获取缺陷二维信息；包括：分别使用八连通域对二值化后的暗场拼接图像和明场拼接图像进行处理，处于同一连通域的像素点构成一个缺陷，使用最小外接矩形框选缺陷，最小外接矩形的长为缺陷的长度，最小外接矩形的宽为缺陷的宽度。Step S3.9: Obtain two-dimensional defect information; including: using eight connected domains to process the binarized dark field stitching image and the bright field stitching image respectively, the pixels in the same connected domain constitute a defect, and the defect is selected using a minimum bounding rectangle, the length of the minimum bounding rectangle is the length of the defect, and the width of the minimum bounding rectangle is the width of the defect. 4.根据权利要求1所述的一种大口径曲面光学元件表面缺陷三维检测方法，其特征在于，待测元件为大口径曲面光学元件。4. A three-dimensional detection method for surface defects of a large-aperture curved optical element according to claim 1, characterized in that the element to be detected is a large-aperture curved optical element.
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