CN109900711A - Workpiece, defect detection method based on machine vision - Google Patents

Workpiece, defect detection method based on machine vision Download PDF

Info

Publication number
CN109900711A
CN109900711A CN201910264717.XA CN201910264717A CN109900711A CN 109900711 A CN109900711 A CN 109900711A CN 201910264717 A CN201910264717 A CN 201910264717A CN 109900711 A CN109900711 A CN 109900711A
Authority
CN
China
Prior art keywords
workpiece
defect
pixel
sub
edge
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.)
Pending
Application number
CN201910264717.XA
Other languages
Chinese (zh)
Inventor
耿磊
魏全生
肖志涛
吴骏
张芳
李文科
刘彦北
王雯
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin Polytechnic University
Original Assignee
Tianjin Polytechnic University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Tianjin Polytechnic University filed Critical Tianjin Polytechnic University
Priority to CN201910264717.XA priority Critical patent/CN109900711A/en
Publication of CN109900711A publication Critical patent/CN109900711A/en
Pending legal-status Critical Current

Links

Abstract

The workpiece, defect detection method based on machine vision that the present invention provides a kind of.This method acquires the image of flange disk workpiece first, camera is demarcated, obtain calibrated error, then the extraction of sub-pixel edge information is carried out to workpiece profile, calculate the distance for being fitted edge to workpiece profile, and by comparing the distance whether be greater than to threshold value differentiation workpiece outer profile breakage, finally aiming at the problem that workpiece surface texture complex effects workpiece surface scratch and corrosion are divided, surface defect is split using the PixelNet convolutional neural networks based on pixel stratified sampling.The result shows that the present invention can accurately detect the appearance defect and surface defect of workpiece, and improve algorithm robustness.

Description

Workpiece, defect detection method based on machine vision
Technical field
The workpiece, defect detection method based on machine vision that the present invention relates to a kind of is using image to flange plate type workpiece When processing technique is detected, detection accuracy and detection efficiency are improved.
Background technique
The computer speed of service is significantly improved from the 1960s, and the appearance of simultaneous CCD technology is based on The defect detecting technique of machine vision starts to be used widely in industrial production line, such as machinery, electronics, printing, weaving row Industry improves product quality and production efficiency by advanced detection technique.A kind of side of the Machine Vision Detection as non-destructive testing Method, it obtains clearly object under test image by linear array or area array cameras, carries out image procossing completion target by computer and lacks Sunken real-time detection.
With the fast development of China's manufacturing industry, the requirement for product quality is also higher and higher, is based on machine in recent years The defects detection theoretical research of vision is constantly mature.Zhou Shan Min et al. obtains metal surface by the way of different angle illumination Multiplex images, defect characteristic has significant difference under different light angles, and the variation between multiple image provides more lack Characteristic information is fallen into, the detection to cracks of metal surface is realized by the related information excavated between multiplex images.This method needs not The disconnected light angle that changes shoots multiple image, but workpiece is in mobile state in the present invention, carries out multi-angle illumination and not only grasps Make cumbersome and can not accomplish real-time detection and sorting to workpiece.Huang Liuqian et al. is asked for the detection of stamping parts appearance defect Topic carries out closed operation to realize the filling of shape damage location, then by the background after closed operation to the background area of extraction Region makes the difference with original image background area, and then the breakage of stamping parts is judged according to size of the difference.Due to workpiece breakage size Difference, therefore the selection of structural elements can not unify when carrying out closed operation to workpiece, to influence the precision of appearance defect detection. Li Yongjing et al., which is proposed, determines workpiece for measurement position based on the matched method of shape template, passes through shape extraction and dynamic threshold Method workpiece configurations defect is split.Since it is using obtaining workpiece image by the way of illuminating, the method is not Detection suitable for the present invention to workpiece configurations defect.Huang Jingwei et al. proposes the image alignment algorithm based on contour feature, To realize the quick alignment of hardware workpiece and template image, and by judging that workpiece for measurement image and standard workpiece image grayscale are poor The size detection Surface Flaw of value.This method is poor to fine scratches and lesser defect Segmentation effect, is unable to satisfy this Real-time detection of the invention to Surface Flaw.
In conclusion there is an urgent need to propose a kind of accuracy height and the stronger workpiece configurations defect of practicability and surface at present The detection method of defect.
Summary of the invention
The purpose of the present invention is overcoming the above-mentioned deficiency of existing method, a kind of workpiece, defect based on machine vision is proposed Detection method, this method may be implemented to greatly improve workpiece configurations defect and surface defect measurement accuracy.For this purpose, of the invention It adopts the following technical scheme that:
Step 1: the image of flange plate type workpiece in acquisition reality;
Step 2: camera being demarcated using Zhang Shi standardization, the parameter and pose of camera are obtained, according to calibration result Obtain the calibrated error of measuring system;
Step 3: image filtering being carried out to workpiece image, extracts area-of-interest;
Step 4: the detection of workpiece pixel edge is completed using Canny operator first, then using based on Gray Moment The sub-pixel edge of method extraction workpiece image;
Step 5: the mass center of concentric holes and via hole on workpiece being obtained using the method for circle fitting, according to the think of of least square method Think, the edge for approaching workpiece outer profile and each via hole is gone to circle;
Step 6: using the defect inspection method based on edge pixel distance, the sub-pix calculated in workpiece actual profile is sat Distance of the punctuate to fitting circle radial direction;
Step 7: surface defect being split using the PixelNet convolutional neural networks based on pixel stratified sampling, is obtained To scratch and corrosion region.
Compared with prior art, the beneficial effects of the present invention are:
For the low problem of workpiece configurations defect and surface defects detection difficulty and detection efficiency, the invention proposes be based on The defect inspection method of machine vision detects the appearance defect of workpiece by Edge Distance method, in conjunction with pixel stratified sampling PixelNet convolutional neural networks are split detection to Surface Flaw.
Compared to traditional detection method, the present invention has many advantages, such as that non-contact, precision is high, adaptability is good, by worker from numerous It is freed in superfluous detection work, greatly improves detection efficiency.The present invention is directed to workpiece outer profile damaged area shape The problem of its segmentation is with identification is influenced with size, the appearance defect detection method based on edge of work distance is proposed, to difference The workpiece of damaged degree has carried out test experience, and the recognition accuracy of outer profile breakage workpiece demonstrates the algorithm up to 100% Validity.In addition, aiming at the problem that workpiece surface texture complex effects workpiece surface scratch and corrosion are divided, using based on pixel The PixelNet convolutional neural networks of stratified sampling are split surface defect, the experimental results showed that the method for the present invention can be right Surface Flaw is effectively divided, the average friendship of segmentation result and ratio is up to 92.3%, therefore, method proposed by the invention It may be implemented to workpiece scratch, corrosion, effective detection of outer profile breakage workpiece, and detection accuracy is high.
Detailed description of the invention
Fig. 1 overall framework schematic diagram;
Fig. 2 flange plate type workpiece image;
Fig. 3 pixel edge extracts result;
Fig. 4 extracts result based on the sub-pixel edge of Gray Moment;
Fig. 5 workpiece breakage position view;
Fig. 6 workpiece breakage position enlarged drawing;
Fig. 7 (a) is accuracy rate change curve, (b) is Loss value change curve;
Fig. 8 corrodes segmentation result;
Fig. 9 scratch segmentation result.
Specific embodiment
The embodiment of the present invention is described in further detail with reference to the accompanying drawing.
Overall framework flow diagram of the invention is as shown in Figure 1.Firstly, acquisition flange disk workpiece image, using Family name's standardization demarcates camera, and then carries out distortion correction to workpiece image;Then it uses using Gaussian filter to figure As being smoothed, convolution operation is carried out using 5 × 5 Gaussian kernels that standard deviation is 1, extracts area-of-interest;It is respectively adopted Canny algorithm, Sobel algorithm, Roberts algorithm and Prewitt algorithm carry out pixel edge detection to image, more various Algorithm detection as a result, the best Canny algorithm of selective extraction effect;Using the Sub-pixel Edge Detection based on Gray Moment Extract the sub-pixel edge of workpiece image;Finally, the mass center of concentric holes and via hole above workpiece is obtained using the method for circle fitting, According to the thought of least square method, the edge for approaching workpiece outer profile and each hole is gone to circle;Workpiece is calculated by Edge Distance Appearance defect;Surface defect is split using the PixelNet convolutional neural networks based on pixel stratified sampling, is obtained The corrosion of workpiece surface and scratch.
1. experimental subjects
Visual field size of the invention is 120mm × 90mm, and selected industrial camera resolution ratio is 1280 × 960, can be calculated The corresponding actual physics distance of each pixel is about 0.09375mm.It is tested using flange plate type workpiece, workpiece configurations are damaged Degree arc length size μ as corresponding to damage location is indicated, tests μ≤1mm respectively, tri- kinds of 2mm and μ >=2mm of μ < of 1mm < The workpiece of different size, Surface Flaw include corrosion and scratch.
2. pixel edge detects
Canny algorithm, Sobel algorithm, Roberts algorithm, Prewitt algorithm is respectively adopted, Pixel-level side is carried out to image Edge detection.Though by Sobel and Roberts edge detection algorithm it can be seen from the extraction result of four kinds of edge detection operators in Fig. 3 The edge of workpiece single pixel can be so obtained, but sweep is poor, there are jagged edges, are unfavorable for the accurate of sub-pixel edge It extracts.For Prewitt operator edge extracting result there are false edge, detection effect is poor.Canny edge detection algorithm is to workpiece Edge extracting is more accurate, while edge-smoothing degree is higher, and the present invention need to measure workpiece size, to edge positioning Required precision is higher, therefore the present invention selects Canny algorithm to the edge extracting of workpiece progress Pixel-level.The visual field size of system For 120mm × 90mm, selected industrial camera resolution ratio is 1280 × 960, can calculate the corresponding actual physics of each pixel away from From about 0.09375mm.There may be the error of 1 pixel between two o'clock in actual measurement, since target of the present invention is examined It surveys precision and is less than 0.1mm, pixel edge is affected to detection accuracy, is unable to satisfy high-precision measurement request.
3. sub-pixel edge extracts
The pixel edge known to 2 is affected to workpiece sensing precision, is unable to satisfy the measurement request of this system.Guarantee In the case that visual field is constant, improving measurement accuracy most straightforward approach can be used the industrial camera of higher resolution to reduce pixel Equivalent, but data volume can be made to increase simultaneously, it is unfavorable for real-time detection.And sub-pixel edge detection can reach 1/n pixel, be equivalent to Measurement accuracy is improved n times.The measurement demand of higher precision can be completed in the case where guaranteeing camera and the constant visual field.Therefore The present invention uses Canny algorithm to complete the detection of workpiece pixel edge first, is then examined again using the edge based on Gray Moment Survey method.Workpiece is detected based on the sub-pixel edge of Gray Moment, sub-pixel edge extracts result as shown in figure 4, using ash When spending moments method extraction workpiece subpixel coordinates, arithmetic accuracy is higher, while sub-pixel edge is smooth, is more suitable for workpiece Asia picture The positioning at plain edge.
4. the defect inspection method based on Edge Distance
The common appearance defect of workpiece is caused mainly due to the incompleteness of edge of work part, on the image show as workpiece Actual edge profile and fitting circle between there are the deviations of certain distance.As shown in figure 5, wherein red edge is the reality of workpiece Border profile, yellow edge are the workpiece profile that fitting obtains.If the collection of sub-pix point is combined into A in workpiece actual profilei(Xi, Yi), I ∈ (1,2,3 ... N), it is known that the center of circle of workpiece fitting circle is O (m, n), by center of circle O (m, n) and AiDetermining linear equation can table It is shown as:Abbreviation obtains: (Xi-m)y+(n-Yi)x-nXi+mYi=0, if the radius of workpiece fitting circle is R,Two coordinate points are calculated, wherein with point AiIt is apart from the smallest point Coordinate points in fitting circle, are denoted as Bi(Pi, Qi).Then sub-pix point A in workpiece actual edgeiAlong fitting circle radial direction to quasi- Close the distance d of profileiIt can pass throughIt finds out.Workpiece is at intact unbroken position, actual wheel Sub-pix point A on exterior featureiWith the radial direction distance d of fitting circleiMuch smaller than the distance of workpiece breakage position.Distance threshold τ is set, Work as diThe sub-pix point A in workpiece actual profile at this time is recorded when > τiThat is workpiece breakage position, thus according to threshold tau is met Coordinate points AiNumber { 0, n } can determine that whether current workpiece is damaged workpiece.
In practical application, it is contemplated that sub-pix point number more (N > 2000) influences the operation speed of algorithm on workpiece profile Degree, the present invention is to sub-pix point set AiIt is sampled in 360 ° relative to workpiece mass center, i.e., by Ai360 parts of equal parts are carried out, A coordinate points ξ is taken out at random in per sub-pix space of points ξ oncei,Constitute new work Part contour pixel point set A 'j, thenThen pass through the pixel collection A ' after samplingjInto Row diCalculating, compared to directly by former ensemble space AiIt is calculated, operation times are greatly decreased, while sub-pix point set A 'j Largely remain former set AiFeature, that is, workpiece profile information, and the differentiation of workpiece breakage situation not will cause It influences, Fig. 6 is the partial enlarged view of workpiece breakage position, A 'jFor the coordinate set after workpiece profile sub-pix point sampling, BiFor Coordinate set in fitting circle corresponding with damage location sub-pix point.Green line segment is then distance of the damage location to fitting circle di.For the robustness for verifying the algorithm, the present invention carries out test experience to the workpiece under different damaged degree, to 100 differences The workpiece of damaged degree is detected, and the recognition accuracy of algorithm is 100% under different damaged degree.
5. the PixelNet convolutional neural networks based on pixel stratified sampling are split surface defect
The present invention is acquired 3000 altogether by vision system and tested with scratch with the flange workpiece for corroding defect.It adopts Region of interesting extraction is carried out to workpiece with traditional image partition method, by workpiece and background separation.By PS software with hand The mode of work mark completes the mark to scratch on workpiece and corrosion.Meanwhile in order to which facilitating for supervised learning training will be on workpiece Scratch be indicated from corrosion with different characteristic values, so that mark figure is converted to index map, the wherein color mark of background Be denoted as (0,0,0), characteristic value 0, the color mark of scratch is (255,0,0), characteristic value 1, the color mark of corrosion be (0, 0,255), characteristic value 2.
Select 2400 pictures as training set from data set, 300 collect as verifying, and 300 are used as test set pair PixelNet network is trained and tests.The training of parted pattern of the present invention uses depth under 7 operating system of Windows The Matlab development interface of learning framework Caffe.In hardware environment, processor is Intel Xeon E5-2625, and video card is NVIDIA GTX1080Ti, video memory size are 11G.Training process is using 24 figures as an iteration step-length, wherein initial learning rate It is set as 0.01, as the increase of exercise wheel number finally decays to 0.0001, the number of iterations is 100,000 times.Standard in training process True rate variation is with penalty values variation as shown in Fig. 7 (a) and Fig. 7 (b).Segmentation result such as Fig. 8 and Fig. 9 institute to corrosion with scratch Show.By Fig. 7 (a) and Fig. 7 (b), the defect based on PixelNet convolutional neural networks that the present invention uses can be intuitively seen Dividing method can complete the accurate segmentation to Surface Flaw.It can accomplish have simultaneously for the different scratch of depth degree Effect is extracted, algorithm robustness with higher.Further to evaluate the method for the present invention to the segmentation performance of workpiece, defect, the present invention The standard evaluation index MIoU of selection semantic segmentation evaluates test result.The model generated using training is on test set It is tested, acquiring MIoU is 93.2%, accuracy rate with higher, can satisfy industry to the detection demand of workpiece, defect.
By judging that the Pixel Information of workpiece segmentation result can determine that workpieces are other, i.e. output result contains red pixel Can be identified as have the workpiece of scratch defects, containing blue pixel then for exist corrosion workpiece.Theoretically qualified workpiece Output result there was only black pixel value, but the case where will appear erroneous segmentation in practical cutting procedure, so that qualified work Part can also have less red or blue pixel value.Setting area threshold alpha is then determined as defect work when dividing defect and being greater than α Otherwise part is qualified workpiece, α value of the present invention is 10 pixels.According to actually detected requirement, the value of α can be finely tuned.
Workpiece, defect detection method proposed by the present invention based on machine vision, achievable shape different damaged degree and table The workpiece sensing of face existing defects, while differentiating that accuracy height, strong robustness can meet the needs of industrial detection.
The foregoing is merely of the invention preferably to apply example, is not intended to limit the scope of the present invention, it should be understood that this Invention is not limited to existing scheme as described herein, and the purpose of these implementations description is to help those skilled in the art The member practice present invention.Any those of skill in the art are easy to carry out without departing from the spirit and scope of the present invention Further improve and perfect, therefore the present invention is only limited by the content and range of the claims in the present invention, is intended to contain Covering all includes alternative and equivalent program in the spirit and scope of the invention being defined by the appended claims.

Claims (3)

1. a kind of workpiece, defect detection method based on machine vision, including the following steps:
Step 1: the image of flange plate type workpiece in acquisition reality;
Step 2: camera being demarcated, the calibrated error of measuring system is obtained;
Step 3: pretreatment being carried out to image and extracts area-of-interest;
Step 4: the sub-pixel edge of image is obtained using Sub-pixel Edge Detection;
Step 5: the theoretical outer edge of flange plate type workpiece is obtained by way of fitting circle;
Step 6: the appearance defect of workpiece is detected using the method based on Edge Distance;
Step 7: surface defect being split using the PixelNet convolutional neural networks based on pixel stratified sampling.
2. the workpiece, defect detection method according to claim 1 based on machine vision, which is characterized in that in step 6, work Part is at intact unbroken position, sub-pix point A in actual profileiWith the radial direction distance d of fitting circleiMuch smaller than workpiece The distance of damage location is arranged distance threshold τ, works as diThe sub-pix point A in workpiece actual profile at this time is recorded when > τiThat is workpiece Damage location, thus according to the coordinate points A for meeting threshold tauiNumber { 0, n } can determine that whether current workpiece is damaged work Part, in practical application, it is contemplated that sub-pix point number more (N > 2000) influences the arithmetic speed of algorithm on workpiece profile, this Invention is to sub-pix point set AiIt is sampled in 360 ° relative to workpiece mass center, i.e., by Ai360 parts of equal parts are carried out, every A coordinate points are taken out at random in sub-pix space of points ξ onceConstitute new workpiece wheel Wide pixel collection A 'j, then(1,2,3 ... 360), then pass through the pixel collection A ' after sampling by j ∈jCarry out di Calculating, compared to directly by former ensemble space AiIt is calculated, operation times are greatly decreased, while sub-pix point set A 'jGreatly Former set A is remained in degreeiFeature, that is, workpiece profile information, and shadow not will cause to the differentiation of workpiece breakage situation It rings.
3. the workpiece, defect detection method according to claim 1 based on machine vision, which is characterized in that in step 7, by It is more in workpiece surface texture, while defect depth degree is different and different, conventional segmentation algorithm can not carry out defect Effective segmentation, the present invention uses PixelNet convolutional neural networks to complete the segmentation to Surface Flaw for the first time, using biography The image partition method of system carries out region of interesting extraction to workpiece, by workpiece and background separation, by PS software with manual mark The mode of note completes the mark to scratch on workpiece and corrosion, meanwhile, for facilitating drawing on workpiece for supervised learning training Trace is indicated from corrosion with different characteristic values, so that mark figure is converted to index map, wherein the color mark of background is (0,0,0), characteristic value 0, the color mark of scratch are (255,0,0), characteristic value 1, the color mark of corrosion be (0,0, 255), characteristic value 2, by judging that the Pixel Information of workpiece segmentation result can determine that workpieces are other, i.e. output result contains The workpiece that can be identified as having scratch defects of red pixel, the workpiece then corroded for presence containing blue pixel, theoretically The output result of qualified workpiece only has black pixel value, but the case where will appear erroneous segmentation in practical cutting procedure, so that Can also there be less red or blue pixel value in qualified workpiece, setting area threshold alpha then determines when dividing defect and being greater than α It is otherwise qualified workpiece for defect workpiece, α value of the present invention is 10 pixels.
CN201910264717.XA 2019-04-02 2019-04-02 Workpiece, defect detection method based on machine vision Pending CN109900711A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910264717.XA CN109900711A (en) 2019-04-02 2019-04-02 Workpiece, defect detection method based on machine vision

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910264717.XA CN109900711A (en) 2019-04-02 2019-04-02 Workpiece, defect detection method based on machine vision

Publications (1)

Publication Number Publication Date
CN109900711A true CN109900711A (en) 2019-06-18

Family

ID=66955230

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910264717.XA Pending CN109900711A (en) 2019-04-02 2019-04-02 Workpiece, defect detection method based on machine vision

Country Status (1)

Country Link
CN (1) CN109900711A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110441318A (en) * 2019-08-22 2019-11-12 郑州大学 A kind of chemical fibre spinneret hole defect inspection method based on machine vision
CN110602462A (en) * 2019-09-27 2019-12-20 南京工程学院 Industrial image detection device and method based on AI
CN110687120A (en) * 2019-09-18 2020-01-14 浙江工商大学 Flange appearance quality detecting system
CN111189854A (en) * 2020-04-13 2020-05-22 征图新视(江苏)科技股份有限公司 Defect layering detection method of automatic glass cover plate detection system
CN112508939A (en) * 2020-12-22 2021-03-16 郑州金惠计算机系统工程有限公司 Flange surface defect detection method, system and equipment
CN112700440A (en) * 2021-01-18 2021-04-23 上海闻泰信息技术有限公司 Object defect detection method and device, computer equipment and storage medium
CN113781430A (en) * 2021-09-09 2021-12-10 北京云屿科技有限公司 Glove surface defect detection method and system based on deep learning
CN114663433A (en) * 2022-05-25 2022-06-24 山东科技大学 Method and device for detecting running state of roller cage shoe, computer equipment and medium

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1898555A (en) * 2003-12-22 2007-01-17 玛机统丽公司 Substrate inspection device
CN101334263A (en) * 2008-07-22 2008-12-31 东南大学 Circular target circular center positioning method
CN104359403A (en) * 2014-11-21 2015-02-18 天津工业大学 Plane part size measurement method based on sub-pixel edge algorithm
CN105701492A (en) * 2014-11-25 2016-06-22 宁波舜宇光电信息有限公司 Machine vision identification system and implementation method thereof
CN105865344A (en) * 2016-06-13 2016-08-17 长春工业大学 Workpiece dimension measuring method and device based on machine vision
CN106952262A (en) * 2017-04-25 2017-07-14 大连理工大学 A kind of deck of boat analysis of Machining method based on stereoscopic vision
US20180293721A1 (en) * 2017-04-07 2018-10-11 Kla-Tencor Corporation Contour based defect detection
CN108830838A (en) * 2018-05-28 2018-11-16 江苏大学 A kind of pcb board incompleteness Trigger jitter detection method of sub-pixel
CN109141232A (en) * 2018-08-07 2019-01-04 常州好迪机械有限公司 A kind of circle plate casting online test method based on machine vision

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1898555A (en) * 2003-12-22 2007-01-17 玛机统丽公司 Substrate inspection device
CN101334263A (en) * 2008-07-22 2008-12-31 东南大学 Circular target circular center positioning method
CN104359403A (en) * 2014-11-21 2015-02-18 天津工业大学 Plane part size measurement method based on sub-pixel edge algorithm
CN105701492A (en) * 2014-11-25 2016-06-22 宁波舜宇光电信息有限公司 Machine vision identification system and implementation method thereof
CN105865344A (en) * 2016-06-13 2016-08-17 长春工业大学 Workpiece dimension measuring method and device based on machine vision
US20180293721A1 (en) * 2017-04-07 2018-10-11 Kla-Tencor Corporation Contour based defect detection
CN106952262A (en) * 2017-04-25 2017-07-14 大连理工大学 A kind of deck of boat analysis of Machining method based on stereoscopic vision
CN108830838A (en) * 2018-05-28 2018-11-16 江苏大学 A kind of pcb board incompleteness Trigger jitter detection method of sub-pixel
CN109141232A (en) * 2018-08-07 2019-01-04 常州好迪机械有限公司 A kind of circle plate casting online test method based on machine vision

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
GENG LEI等: "Machine Vision Detection Method for Surface Defects of Automobile Stamping Parts", 《AMERICAN SCIENTIFIC RESEARCH JOURNAL FOR ENGINEERING, TECHNOLOGY, AND SCIENCES》 *
JIAN CHUANXIA等: "Automatic surface defect detection for mobile phone screen glass based on machine vision", 《APPLIED SOFT COMPUTING》 *
李帅等: "基于机器视觉的纽扣缺陷检测算法研究", 《科技创新与应用》 *
李长云等: "《智能感知技术及在电气工程中的应用》", 31 May 2017, 电子科技大学出版社 *
汤勃等: "机器视觉表面缺陷检测综述", 《中国图象图形学报》 *
耿磊等: "上下边缘区分的平面钣金零件尺寸测量方法", 《红外与激光工程》 *
郑树泉等: "《工业智能技术与应用》", 31 January 2019, 上海科学技术出版社 *
郭联金等: "基于亚像素的PCB表面质量检测", 《电子质量》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110441318A (en) * 2019-08-22 2019-11-12 郑州大学 A kind of chemical fibre spinneret hole defect inspection method based on machine vision
CN110687120A (en) * 2019-09-18 2020-01-14 浙江工商大学 Flange appearance quality detecting system
CN110602462A (en) * 2019-09-27 2019-12-20 南京工程学院 Industrial image detection device and method based on AI
CN110602462B (en) * 2019-09-27 2020-11-03 南京工程学院 Industrial image detection device and method based on AI
CN111189854A (en) * 2020-04-13 2020-05-22 征图新视(江苏)科技股份有限公司 Defect layering detection method of automatic glass cover plate detection system
CN111189854B (en) * 2020-04-13 2020-08-07 征图新视(江苏)科技股份有限公司 Defect layering detection method of automatic glass cover plate detection system
CN112508939A (en) * 2020-12-22 2021-03-16 郑州金惠计算机系统工程有限公司 Flange surface defect detection method, system and equipment
CN112700440A (en) * 2021-01-18 2021-04-23 上海闻泰信息技术有限公司 Object defect detection method and device, computer equipment and storage medium
CN113781430A (en) * 2021-09-09 2021-12-10 北京云屿科技有限公司 Glove surface defect detection method and system based on deep learning
CN114663433A (en) * 2022-05-25 2022-06-24 山东科技大学 Method and device for detecting running state of roller cage shoe, computer equipment and medium

Similar Documents

Publication Publication Date Title
CN109900711A (en) Workpiece, defect detection method based on machine vision
CN109141232B (en) Online detection method for disc castings based on machine vision
CN105335973B (en) Apply to the visual processing method of strip machining production line
CN104101600B (en) Cross Section of CC Billet testing of small cracks method
CN104050446A (en) Meter pointer image identification method based on pointer width character
WO2017181724A1 (en) Inspection method and system for missing electronic component
CN109856156A (en) A kind of display panel tiny flaw determination method and device based on AOI
CN106780526A (en) A kind of ferrite wafer alligatoring recognition methods
CN109540925B (en) Complex ceramic tile surface defect detection method based on difference method and local variance measurement operator
CN110807355A (en) Pointer instrument detection and reading identification method based on mobile robot
CN112037203A (en) Side surface defect detection method and system based on complex workpiece outer contour registration
CN107133962A (en) A kind of diamond saw blade extracting thermal crack method based on rim detection
CN105023018A (en) Jet code detection method and system
CN110108712A (en) Multifunctional visual sense defect detecting system
CN103337067B (en) The visible detection method of single needle scan-type screw measurement instrument probe X-axis rotating deviation
CN108492306A (en) A kind of X-type Angular Point Extracting Method based on image outline
CN112819844A (en) Image edge detection method and device
CN111833350A (en) Machine vision detection method and system
CN113792725B (en) Component detection device and method
CN104966302B (en) A kind of detection localization method of any angle laser cross
CN113340909B (en) Glue line defect detection method based on machine vision
CN110189375A (en) A kind of images steganalysis method based on monocular vision measurement
CN108520514B (en) Consistency detection method for electronic elements of printed circuit board based on computer vision
CN112837285B (en) Edge detection method and device for panel image
KR20190119801A (en) Vehicle Headlight Alignment Calibration and Classification, Inspection of Vehicle Headlight Defects

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
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20190618

WD01 Invention patent application deemed withdrawn after publication