CN111929311B - One-stop intelligent defect detection system - Google Patents
One-stop intelligent defect detection system Download PDFInfo
- Publication number
- CN111929311B CN111929311B CN202011099840.XA CN202011099840A CN111929311B CN 111929311 B CN111929311 B CN 111929311B CN 202011099840 A CN202011099840 A CN 202011099840A CN 111929311 B CN111929311 B CN 111929311B
- Authority
- CN
- China
- Prior art keywords
- image
- defect
- defect detection
- detection
- stop
- 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.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 167
- 230000007547 defect Effects 0.000 title claims abstract description 151
- 230000001133 acceleration Effects 0.000 claims abstract description 37
- 239000000463 material Substances 0.000 claims abstract description 25
- 238000004891 communication Methods 0.000 claims abstract description 10
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 238000003860 storage Methods 0.000 claims abstract description 4
- 230000002950 deficient Effects 0.000 claims description 32
- 238000000605 extraction Methods 0.000 claims description 21
- 239000011159 matrix material Substances 0.000 claims description 14
- 230000004927 fusion Effects 0.000 claims description 10
- HPTJABJPZMULFH-UHFFFAOYSA-N 12-[(Cyclohexylcarbamoyl)amino]dodecanoic acid Chemical compound OC(=O)CCCCCCCCCCCNC(=O)NC1CCCCC1 HPTJABJPZMULFH-UHFFFAOYSA-N 0.000 claims description 9
- 230000015654 memory Effects 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 8
- 230000000875 corresponding effect Effects 0.000 claims description 6
- 238000001303 quality assessment method Methods 0.000 claims description 6
- 230000009471 action Effects 0.000 claims description 4
- 238000013441 quality evaluation Methods 0.000 claims description 4
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 abstract description 7
- 238000005286 illumination Methods 0.000 abstract description 6
- 230000000694 effects Effects 0.000 abstract description 5
- 230000000007 visual effect Effects 0.000 abstract description 4
- 238000011179 visual inspection Methods 0.000 abstract description 3
- 230000006872 improvement Effects 0.000 description 7
- 238000011161 development Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 239000003292 glue Substances 0.000 description 2
- 238000009776 industrial production Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 239000002699 waste material Substances 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
-
- 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
- G01N21/8851—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
- Chemical & Material Sciences (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention relates to a one-stop intelligent defect detection system, which comprises an image acquisition part, an intelligent mode self-adaption part, a one-stop defect detection part, a parallel acceleration part, a defect storage and communication part, a control part and a defect identification execution part. The invention can detect all defect types covered in the visual field range in a one-stop mode and give the position information of the defect area; and a complex defect detection tool is not required to be used for carrying out a combined and superposed defect detection means. The invention can carry out self-adaptive detection mode matching according to the material quality and the detection requirement of the detected material so as to achieve the optimal defect detection effect. The one-stop defect detection algorithm is suitable for parallel acceleration calculation, and can easily perform one-stop online, rapid and real-time defect detection by introducing a parallel acceleration part. The one-stop defect detection algorithm has very strong robustness to environmental factors such as illumination conditions and the like. The most painful environmental problem for industrial visual inspection is light exposure.
Description
Technical Field
The invention relates to a one-stop intelligent defect detection system, and belongs to the technical field of intelligent defect detection.
Background
At present, most of the market share of the demand of defect detection is distributed in the industrial production field, and the defect detection of most industries in the industrial production field is still in the artificial naked eye detection stage, so that enterprises need to invest a large amount of manpower, material resources and financial resources to ensure the quality of delivered products. At present, the mainstream visual defect detection system in the market mostly adopts a complex defect detection tool to implement a combined and superposed detection means; or a special detection tool is developed aiming at a single industry, and the detection tool is poor in universality.
Therefore, the patent provides a one-stop intelligent defect detection system, which can carry out intelligent detection mode matching according to the material to be detected, and can easily carry out one-stop online, quick and real-time defect detection by introducing a parallel acceleration part.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a one-stop intelligent defect detection system, which has the following specific technical scheme:
a one-stop intelligent defect detection system, comprising:
the image acquisition part consists of a camera, a lens and a lighting subsystem, and is used for acquiring an original image of the detected material;
the intelligent mode self-adaptive part is realized by a detection mode self-adaptive algorithm, and the detection mode self-adaptive algorithm realizes closed-loop control by constructing a defect detection result quality evaluation function;
the one-stop defect detection part is realized by a one-stop defect detection algorithm, and the one-stop defect detection algorithm is used for realizing all defect types and defect position information covered in the camera view field;
the parallel acceleration part comprises a GPU or FPGA chip and a parallel acceleration algorithm module;
the defect storage and communication part is realized by a database and a communication module and is used for storing the defect detection result in real time;
a control section for receiving a defect detection result;
and the defect identification executing part is used for receiving the control signal sent by the control part and making a corresponding action to carry out defect identification.
As an improvement of the above technical solution, the operation flow of the one-stop intelligent defect detecting system includes the following steps:
step 2, collecting a plurality of defective images;
step 3, deploying defect detection and starting on line, and intelligently matching the detection mode through the good product image and the defective product image by the intelligent mode self-adaptive part;
step 4, one-stop normal defect detection and parallel calculation acceleration are carried out;
step 5, storing the defect detection result and sending detection information to the control part in a communication way;
step 6, the control part receives the detection information and sends a related control signal;
and 7, the defect identification executing part receives the control signal sent by the control part and makes a corresponding executing action.
As an improvement of the above technical solution, the resolution of the images involved in the one-stop defect detection algorithm must be consistent, otherwise normal defect detection cannot be performed; if the resolution ratios of the collected images are not consistent in practical application, defect detection is carried out through advanced resolution ratio unified conversion, and the method comprises the following steps:
wherein,
is the height of the acquired image;
width of the acquired image;
to obtain the image height;
to obtain the image width;
to input good images.
As an improvement of the above technical solution, the detection mode adaptive algorithm includes the following steps:
step 1.1, CollectionnOpening a good product image of the detected material,nthe collected good product images are weighted and fused to obtain a good product template image as a positive integer
，
CollectingmOpening a defective product image of the detected material,mis a positive integer, and the acquired defective product images are weighted and fused to obtain a defective product template image
，
Wherein,irepresenting the number of the collected images;ncollecting the total number of good images;mthe total number of the collected defective images is counted;
the images are collected for different good products;
the collected images of different defective products;
obtaining a good product template image through weighted fusion;
obtaining a defective product template image through weighted fusion; g () is a weighted fusion operator;
step 1.2, selecting a detection mode
To good product template image
According to a mode
Performing feature extraction operation to obtain feature images of good-quality template images
(ii) a For defective product template image
According to a mode
Performing feature extraction operation to obtain feature image of defective product template image
(ii) a To be detected image
According to a mode
Performing feature extraction operation to obtain feature image of the detected image
；
Wherein,
indicating a detection mode
Corresponding feature extraction operators;
step 1.3, detecting mode
Characteristic image of
,
Carrying out matrix subtraction and solving the absolute value of the matrix subtraction to obtain a difference characteristic image
(ii) a For difference characteristic image
Performing small kernel convolution operation to obtain defect position, and marking the defect position to the detected image
Corresponding to the spatial position to obtain a detection identification image
；
Wherein,Conv.() To representThe convolution identification operator is used for carrying out convolution operation on the difference value characteristics to obtain a defect position and restoring the defect position information to the detected image for identification;
step 1.3, exhausting all detection modes
And repeating the steps 1.1 to 1.3 to obtain an optimal mode
；
Wherein,
in order to detect the mode of the light,
represents an optimal quality assessment value;f 1()、f 2() Representing a quality assessment operator;
、
in order to be the weighting coefficients,
；
representing a positive integer.
As an improvement of the above technical solution, the one-stop defect detection algorithm includes the following steps:
step 2.1, deploying actual detection on line and using an optimal mode
Calculating the images of the good templates
Characteristic image of
For real-time single detected image
According to the optimal mode
Performing feature extraction to obtain image
，
Wherein,
is an optimal mode
Corresponding feature is providedTaking a descriptor;
step 2.2, optimizing the mode
Characteristic image of
,
Carrying out matrix subtraction and solving the absolute value of the matrix subtraction to obtain a difference characteristic image
(ii) a For difference characteristic image
Performing small kernel convolution and identification operation to obtain defect detection result image
(ii) a Defect detection result image
The method comprises the steps of containing position information of all defect areas in an image, and carrying out differentiated color highlight identification on the defect positions;
as an improvement of the technical scheme, the parallel acceleration algorithm module is accelerated by adopting GPU hardware and a CUDA acceleration algorithm library in an industrial personal computer environment; the parallel acceleration algorithm module performs parallel acceleration by using an FPGA chip under an embedded system environment.
As an improvement of the technical scheme, the step of using the CUDA acceleration algorithm library comprises the following steps:
step 3.1, allocating host memories and initializing data;
step 3.2, allocating device memories, and copying data to the devices from the host;
3.3, calling a kernel function of the CUDA to complete specified operation on the device;
step 3.4, copying the operation result on the device to a host;
and 3.5, releasing the memory allocated on the device and the host.
The invention has the beneficial effects that:
1) one-stop detection: the invention can detect all defect types covered in the visual field range in a one-stop mode and give the position information of the defect area; and a complex defect detection tool is not required to be used for carrying out a combined and superposed defect detection means.
2) Intelligent mode adaptation: the invention can carry out self-adaptive detection mode matching according to the material quality and the detection requirement of the detected material so as to achieve the optimal defect detection effect. The user can also carry out secondary development and add a user-defined detection mode, and the user-defined detection mode can also support the matching of the self-adaptive detection mode.
3) And quick: the one-stop defect detection algorithm is suitable for parallel acceleration calculation, and can easily perform one-stop online, rapid and real-time defect detection by introducing a parallel acceleration part.
4) High robustness: the one-stop defect detection algorithm has very strong robustness to environmental factors such as illumination conditions and the like. The most painful environmental problem for industrial visual inspection is light exposure.
Drawings
FIG. 1 is a flow chart of a one-stop intelligent defect detection system according to the present invention;
FIG. 2 is a block diagram of an algorithm module of the one-stop intelligent defect detection system according to the present invention;
FIG. 3 is an original image for product inspection;
FIG. 4 is a graph of high intensity illumination condition defect detection;
fig. 5 is a diagram of the detection effect of low-brightness lighting conditions.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, the one-stop intelligent defect detection system includes:
the image acquisition part consists of a camera, a lens and a lighting subsystem, and is used for acquiring an original image of the detected material; the method can acquire the high-quality and high-contrast original image of the detected material.
The intelligent mode self-adaptive part is realized by a detection mode self-adaptive algorithm, the detection mode self-adaptive algorithm realizes closed-loop control by constructing a defect detection result quality evaluation function, and the intelligent selection and switching of the detection modes can be realized aiming at different detected materials.
The one-stop defect detection part is realized by a one-stop defect detection algorithm, and the one-stop defect detection algorithm is used for realizing all defect types and defect position information covered in the camera view field; meanwhile, the one-stop defect detection algorithm can intelligently select different detection modes, including a user-defined detection mode added by secondary development of a user.
The parallel acceleration part comprises a GPU or FPGA chip and a parallel acceleration algorithm module; the real-time defect detection requirement of the high-resolution image can be easily met through parallel computing and acceleration.
The defect storage and communication part is realized by a database and a communication module and is used for storing the defect detection result in real time; the detection result may also be communicated to the control section.
And a control section for receiving the defect detection result.
And the defect identification executing part is used for receiving the control signal sent by the control part and making a corresponding action to carry out defect identification.
Example 2
The operation flow of the one-stop intelligent defect detection system comprises the following steps:
step 2, collecting a plurality of defective images;
step 3, deploying defect detection and starting on line, and intelligently matching the detection mode through the good product image and the defective product image by the intelligent mode self-adaptive part;
step 4, one-stop normal defect detection and parallel calculation acceleration are carried out;
step 5, storing the defect detection result and sending detection information to the control part in a communication way;
step 6, the control part receives the detection information and sends a related control signal;
and 7, the defect identification executing part receives the control signal sent by the control part and makes a corresponding executing action.
The defect mark executing part can be specifically a cylinder with a quick-dry marking pen, an ink jet printer, a coding machine and other mechanisms. The actions performed may be: and marking characters such as 'NG' or 'defective product' and 'unqualified product' on the defective product.
Example 3
The one-stop defect detection algorithm is realized by the following steps:
the one-stop defect detection algorithm can select different detection modes to adapt to detected materials of different materials, the detection mode self-adaptive algorithm can automatically select an optimal detection mode, and the parallel acceleration algorithm module provides strong calculation support for the one-stop defect detection algorithm.
Because the image acquisition part is determined, the acquired image resolutions are consistent, the image resolutions (height and width) related in the one-stop defect detection algorithm are required to be consistent, otherwise, normal defect detection cannot be carried out; if the resolution ratios of the collected images are not consistent in practical application, defect detection is carried out through advanced resolution ratio unified conversion, and the method comprises the following steps:
wherein,
is the height of the acquired image;
width of the acquired image;
to obtain the image height;
to obtain the image width;
to input good images.
Example 4
The detection mode self-adaptive algorithm intelligently selects and switches the optimal detection mode of the detected material.
As shown in fig. 2, the detection mode adaptive algorithm includes the following steps:
step 1.1, CollectionnOpening a good product image of the detected material,nthe collected good product images are weighted and fused to obtain a good product template image as a positive integer
，
CollectingmOpening a defective product image of the detected material,mis a positive integer, and the acquired defective product images are weighted and fused to obtain a defective product template image
，
Wherein,irepresenting the number of the collected images;ncollecting the total number of good images;mthe total number of the collected defective images is counted;
the images are collected for different good products;
the collected images of different defective products;
obtaining a good product template image through weighted fusion;
obtaining a defective product template image through weighted fusion; g () is a weighted fusion operator;
step 1.2, selecting a detection mode
To good product template image
According to a mode
Performing feature extraction operation to obtain feature images of good-quality template images
(ii) a For defective product template image
According to a mode
Performing feature extraction operation to obtain feature image of defective product template image
(ii) a To be detected image
According to a mode
Performing feature extraction operation to obtain feature image of the detected image
；
Wherein,
indicating a detection mode
Corresponding feature extraction operators;
step 1.3, detecting mode
Characteristic image of
,
Carrying out matrix subtraction and solving the absolute value of the matrix subtraction to obtain a difference characteristic image
(ii) a For difference characteristic image
Performing small kernel convolution operation to obtain defect position, and marking the defect position to the detected image
Corresponding to the spatial position to obtain a detection identification image
；
Wherein,Conv.() Representing a convolution identification operator, carrying out convolution operation on the difference value characteristics to obtain a defect position, and restoring the defect position information to the detected image to carry out identification;
step 1.3, exhausting all detection modes
And repeating the steps 1.1 to 1.3 to obtain an optimal mode
；
Wherein,
for the detection mode, different detection modes correspond to different feature extraction methods, here, an equivalent image feature descriptor extraction function can be used, and by constructing a quality evaluation function, an optimal detection mode is selected from a plurality of detection modes
。
Wherein,
represents an optimal quality assessment value;f 1()、f 2() Representing a quality assessment operator;
、
for weighting factors, empirical values may be taken
；
Representing a positive integer.
Example 5
The one-stop defect detection algorithm comprises the following steps:
step 2.1, deploying actual detection on line and using an optimal mode
Calculating the images of the good templates
Characteristic image of
For real-time single detected image
According to the optimal mode
Performing feature extraction to obtain image
，
Wherein,
is an optimal mode
Corresponding feature extraction descriptors;
step 2.2, optimizing the mode
Characteristic image of
,
Carrying out matrix subtraction and solving the absolute value of the matrix subtraction to obtain a difference characteristic image
(ii) a For difference characteristic image
Performing small kernel convolution and identification operation to obtain defect detection result image
(ii) a Defect detection result image
The method comprises the steps of containing position information of all defect areas in an image, and carrying out differentiated color highlight identification on the defect positions;
since the algorithm is based on the same detection mode
Extracting features, and performing matrix subtraction to obtain difference feature image
Therefore, the algorithm has natural high robustness to interference of environmental factors such as light.
The robustness of the algorithm to illumination can be seen from the overall flow of image processing (PipeLine). 3-5 are graphs providing a comparison of the detection effect of the algorithm before and after a change in ambient light; as can be seen from fig. 3-5: the algorithm is proved to be applicable to different illumination conditions.
Example 6
The one-stop defect detection algorithm applies a large number of matrix addition, subtraction, multiplication and division operations, and is suitable for parallel accelerated calculation. The parallel acceleration algorithm module adopts GPU hardware and a CUDA acceleration algorithm library to accelerate in an industrial personal computer environment; the parallel acceleration algorithm module performs parallel acceleration by using an FPGA chip under an embedded system environment.
The steps of using the CUDA acceleration algorithm library are as follows:
step 3.1, allocating host memories and initializing data;
step 3.2, allocating device memories, and copying data to the devices from the host;
3.3, calling a kernel function of the CUDA to complete specified operation on the device;
step 3.4, copying the operation result on the device to a host;
and 3.5, releasing the memory allocated on the device and the host.
Wherein, host can be regarded as a logic control unit, usually a CPU; device can be thought of as a parallel computing acceleration device, commonly referred to as a GPU.
In the above embodiments, the present invention provides a one-stop intelligent defect inspection system, which can inspect all defect types and defect position information covered in the field of view in one-stop manner without using the complex defect inspection tools to perform the combined and overlapped inspection means. The system can intelligently switch the detection modes according to the material attribute and the detection requirement of the detected material, achieves the requirement of being compatible with different material defect detection, and can also support a user to carry out secondary development and add a self-defined detection mode. The one-stop defect detection method is suitable for parallel accelerated computation, and the speed bottleneck problem of high-resolution image online deployment real-time detection is perfectly solved by adding a parallel computation part. For example: 500 ten thousand color images, wherein 500ms is needed for processing one image under the condition of no acceleration, the frame rate is 2fps, the product jump distance (the length of one product) is assumed to be 160mm, and the running speed is 19.2 m/min; acceleration was 200ms, 5fps, assuming a product jump (length of one piece of product) of 160mm, and a running speed of 48 m/min.
The invention has the following advantages:
1) one-stop detection: the invention can detect all defect types (defect types comprise hole blockage, incomplete waste discharge, scratch, dirt, foreign matters, glue deficiency, glue overflow, bubbles, folds, sheet overlapping and the like, most defect types have the characteristics of random positions, different sizes and shapes and the like) covered in a visual field in a one-stop mode, and provides position information of a defect area. And a complex defect detection tool is not required to be used for carrying out a combined and superposed defect detection means.
The core detection idea of the invention is to adopt a full-width characteristic image comparison mode to carry out 'full-width' comparison on a standard qualified product and a detected product, so that all defects covered by the whole image can be detected in a one-stop mode. (full comparison: the meaning of the full characteristic image is different according to the different detection modes, the full characteristic image can be simply understood as a characteristic diagram, the characteristic extraction methods under different modes are different, developers can add the characteristic extraction methods, and the method can provide a brand-new image processing flow of surface defect detection, namely standard sample collection, positioning, full characteristic diagram comparison and small convolution kernel identification of defect positions).
2) Intelligent mode adaptation: the invention can carry out self-adaptive detection mode matching according to the material quality and the detection requirement of the detected material so as to achieve the optimal defect detection effect. The user can also carry out secondary development and add a user-defined detection mode, and the user-defined detection mode can also support the matching of the self-adaptive detection mode.
3) And quick: the one-stop defect detection algorithm is suitable for parallel acceleration calculation, and can easily perform one-stop online, rapid and real-time defect detection by introducing a parallel acceleration part.
4) High robustness: the one-stop defect detection algorithm has very strong robustness to environmental factors such as illumination conditions and the like. The most painful environmental problem for industrial visual inspection is light exposure.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A one-stop intelligent defect detection system, comprising:
the image acquisition part consists of a camera, a lens and a lighting subsystem, and is used for acquiring an original image of the detected material;
the intelligent mode self-adaptive part is realized by a detection mode self-adaptive algorithm, and the detection mode self-adaptive algorithm realizes closed-loop control by constructing a defect detection result quality evaluation function;
the one-stop defect detection part is realized by a one-stop defect detection algorithm, and the one-stop defect detection algorithm is used for realizing all defect types and defect position information covered in the camera view field;
the parallel acceleration part comprises a GPU or FPGA chip and a parallel acceleration algorithm module;
the defect storage and communication part is realized by a database and a communication module and is used for storing the defect detection result in real time;
a control section for receiving a defect detection result;
the defect identification executing part is used for receiving the control signal sent by the control part and making a corresponding action to carry out defect identification;
the detection mode adaptive algorithm comprises the following steps:
step 1.1, CollectionnOpening a good product image of the detected material,nthe collected good product images are weighted and fused to obtain a good product template image as a positive integer
,
CollectingmOpening a defective product image of the detected material,mis a positive integer, and carries out weighted fusion on the collected defective product images to obtain the defective productTemplate image
，
Wherein,irepresenting the number of the collected images;ncollecting the total number of good images;mthe total number of the collected defective images is counted;
the images are collected for different good products;
the collected images of different defective products;
obtaining a good product template image through weighted fusion;
obtaining a defective product template image through weighted fusion; g () is a weighted fusion operator;
step 1.2, selecting a detection mode
To good product template image
According to a mode
Performing feature extraction operation to obtain a good product template diagramCharacteristic image of image
(ii) a For defective product template image
According to a mode
Performing feature extraction operation to obtain feature image of defective product template image
(ii) a To be detected image
According to a mode
Performing feature extraction operation to obtain feature image of the detected image
；
Wherein,
indicating a detection mode
Corresponding feature extraction operators;
step 1.3, detecting mode
Characteristic image of
,
Carrying out matrix subtraction and solving the absolute value of the matrix subtraction to obtain a difference characteristic image
(ii) a For difference characteristic image
Performing small kernel convolution operation to obtain defect position, and marking the defect position to the detected image
Corresponding to the spatial position to obtain a detection identification image
；
Wherein,Conv.() Representing a convolution identification operator, carrying out convolution operation on the difference value characteristics to obtain a defect position, and restoring the defect position information to the detected image to carry out identification;
step 1.3, exhausting all detection modes
And repeating the steps 1.1 to 1.3 to obtain an optimal mode
；
Wherein,
in order to detect the mode of the light,
represents an optimal quality assessment value;f 1()、f 2() Representing a quality assessment operator;
、
in order to be the weighting coefficients,
；
represents a positive integer;
the one-stop defect detection algorithm comprises the following steps:
step 2.1, deploying actual detection on line and using an optimal mode
Calculating the images of the good templates
Characteristic image of
For real-time single detected image
According to the optimal mode
Performing feature extraction to obtain image
，
Wherein,
is an optimal mode
Corresponding feature extraction descriptors;
step 2.2, optimizing the mode
Characteristic image of
,
Carrying out matrix subtraction and solving the absolute value of the matrix subtraction to obtain a difference characteristic image
(ii) a For difference characteristic image
Performing small kernel convolution and identification operation to obtain defect detection result image
(ii) a Defect detection result image
The method comprises the steps of containing position information of all defect areas in an image, and carrying out differentiated color highlight identification on the defect positions;
2. the one-stop intelligent defect detecting system of claim 1, wherein the operation flow of the one-stop intelligent defect detecting system comprises the following steps:
step 1, collecting a plurality of images of good products;
step 2, collecting a plurality of defective images;
step 3, deploying defect detection and starting on line, and intelligently matching the detection mode through the good product image and the defective product image by the intelligent mode self-adaptive part;
step 4, one-stop normal defect detection and parallel calculation acceleration are carried out;
step 5, storing the defect detection result and sending detection information to the control part in a communication way;
step 6, the control part receives the detection information and sends a related control signal;
and 7, the defect identification executing part receives the control signal sent by the control part and makes a corresponding executing action.
3. The system of claim 1, wherein the resolution of the images involved in the one-stop defect detection algorithm must be consistent, otherwise normal defect detection is not possible; if the resolution ratios of the collected images are not consistent in practical application, defect detection is carried out through advanced resolution ratio unified conversion, and the method comprises the following steps:
wherein,
is the height of the acquired image;
width of the acquired image;
to obtain the image height;
to obtain the image width;
to input good images.
4. The one-stop intelligent defect detection system according to claim 1, wherein the parallel acceleration algorithm module is accelerated by adopting GPU hardware and a CUDA acceleration algorithm library in an industrial personal computer environment; the parallel acceleration algorithm module performs parallel acceleration by using an FPGA chip under an embedded system environment.
5. The one-stop intelligent defect detection system of claim 4, wherein the step of using the CUDA acceleration algorithm library comprises:
step 3.1, allocating host memories and initializing data;
step 3.2, allocating device memories, and copying data to the devices from the host;
3.3, calling a kernel function of the CUDA to complete specified operation on the device;
step 3.4, copying the operation result on the device to a host;
and 3.5, releasing the memory allocated on the device and the host.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011099840.XA CN111929311B (en) | 2020-10-15 | 2020-10-15 | One-stop intelligent defect detection system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011099840.XA CN111929311B (en) | 2020-10-15 | 2020-10-15 | One-stop intelligent defect detection system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111929311A CN111929311A (en) | 2020-11-13 |
| CN111929311B true CN111929311B (en) | 2021-01-05 |
Family
ID=73334789
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011099840.XA Active CN111929311B (en) | 2020-10-15 | 2020-10-15 | One-stop intelligent defect detection system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111929311B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116773528B (en) * | 2023-05-16 | 2024-05-10 | 安徽省高速公路试验检测科研中心有限公司 | Visual defect detection method and system for candidate region |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102981467A (en) * | 2012-11-13 | 2013-03-20 | 上海交通大学 | One-stop general information integration monitoring system |
| CN104156913A (en) * | 2014-07-15 | 2014-11-19 | 电子科技大学 | Automatic optic inspection method for surface defects of metal cylindrical workpieces |
| WO2016092640A1 (en) * | 2014-12-10 | 2016-06-16 | 株式会社 日立ハイテクノロジーズ | Defect observation device and defect observation method |
| JP2018205005A (en) * | 2017-05-31 | 2018-12-27 | 株式会社キーエンス | Image inspection device |
| CN110766664A (en) * | 2019-09-29 | 2020-02-07 | 杭州电子科技大学 | A detection method for defective appearance of electronic components based on deep learning |
| CN110895716A (en) * | 2018-09-12 | 2020-03-20 | 发那科株式会社 | Inspection apparatus and machine learning method |
| CN111476306A (en) * | 2020-04-10 | 2020-07-31 | 腾讯科技（深圳）有限公司 | Object detection method, device, equipment and storage medium based on artificial intelligence |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9726617B2 (en) * | 2013-06-04 | 2017-08-08 | Kla-Tencor Corporation | Apparatus and methods for finding a best aperture and mode to enhance defect detection |
-
2020
- 2020-10-15 CN CN202011099840.XA patent/CN111929311B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102981467A (en) * | 2012-11-13 | 2013-03-20 | 上海交通大学 | One-stop general information integration monitoring system |
| CN104156913A (en) * | 2014-07-15 | 2014-11-19 | 电子科技大学 | Automatic optic inspection method for surface defects of metal cylindrical workpieces |
| WO2016092640A1 (en) * | 2014-12-10 | 2016-06-16 | 株式会社 日立ハイテクノロジーズ | Defect observation device and defect observation method |
| JP2018205005A (en) * | 2017-05-31 | 2018-12-27 | 株式会社キーエンス | Image inspection device |
| CN110895716A (en) * | 2018-09-12 | 2020-03-20 | 发那科株式会社 | Inspection apparatus and machine learning method |
| CN110766664A (en) * | 2019-09-29 | 2020-02-07 | 杭州电子科技大学 | A detection method for defective appearance of electronic components based on deep learning |
| CN111476306A (en) * | 2020-04-10 | 2020-07-31 | 腾讯科技（深圳）有限公司 | Object detection method, device, equipment and storage medium based on artificial intelligence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111929311A (en) | 2020-11-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114283111A (en) | Qualitative or quantitative characterization of the surface of a coating | |
| Benedek et al. | Solder paste scooping detection by multilevel visual inspection of printed circuit boards | |
| CN109492638A (en) | Method for text detection, device and electronic equipment | |
| JP7316731B2 (en) | Systems and methods for detecting and classifying patterns in images in vision systems | |
| CN112802016A (en) | Real-time cloth defect detection method and system based on deep learning | |
| CN115775236B (en) | Method and system for visual inspection of surface micro-defects based on multi-scale feature fusion | |
| CN109544522A (en) | A kind of Surface Defects in Steel Plate detection method and system | |
| CN110930356B (en) | An industrial two-dimensional code reference-free quality assessment system and method | |
| CN112651966A (en) | Printed circuit board micro-defect detection method based on ACYOLOV4_ CSP | |
| CN103439338A (en) | Classification method for film defects | |
| CN114494223A (en) | PCB defect detection model training method and device, electronic equipment and storage medium | |
| CN113780484B (en) | Industrial product defect detection method and device | |
| CN112819756A (en) | PCB surface defect detection device and method | |
| CN114266764A (en) | Character integrity detection method and device for printed label | |
| CN113034488A (en) | Visual detection method of ink-jet printed matter | |
| CN111929311B (en) | One-stop intelligent defect detection system | |
| CN114372980A (en) | Industrial defect detection method and system | |
| CN115731198A (en) | An intelligent detection system for leather surface defects | |
| CN117173154A (en) | Online image detection system and method for glass bottle | |
| CN109087292A (en) | A kind of thermal transfer film glue deviation detection method based on machine vision | |
| CN109886936B (en) | A low-contrast defect detection method and device | |
| CN104881641A (en) | Mobile device based digital recognition method and system for questionnaires and tables | |
| CN114972246A (en) | A deep learning-based surface defect detection method for die-cutting products | |
| CN114612399A (en) | A picture recognition system and method for mobile phone appearance marking | |
| CN114612418A (en) | Method and device, system, and electronic device for detecting surface defects of mouse shell |
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 |