CN103049753A - Method for detecting defects of printed circuit board (PCB) based on skeleton extraction and range conversion - Google Patents
Method for detecting defects of printed circuit board (PCB) based on skeleton extraction and range conversion Download PDFInfo
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Abstract
The invention discloses a method for detecting defects of a printed circuit board (PCB) based on skeleton extraction and range conversion. The method comprises the following steps of: checking an index table to extract a single-pixel skeleton; deleting skeleton branches; and searching for range value domain abnormity, and classifying PCB defects according to abnormal conditions. According to the method for detecting defects of the PCB based on skeleton extraction and range conversion, useless points can be deleted on the premise of not influencing the quantity of connected domains by extracting the single-pixel skeleton in a way of checking the index table, and the extracted skeleton is of a single pixel and has high generality and high practicability; skeleton branches are deleted, and range value domain abnormity is searched, so that calculation deviations can be reduced without influencing the speed and accuracy of skeleton tracking; and branches can be deleted, so that the calculation amount of skeleton search is reduced, and the efficiency of defect identification is increased.
Description
Technical field
The present invention relates to detect the field of defect of printed circuit board, relate in particular to a kind of method that detects defect of printed circuit board based on skeletal extraction and range conversion.
Background technology
Printed circuit board (PCB) is the critical piece of various electronic products, and the title of " mother of electronic product " is arranged, and it is that any electronic equipment and product all need be equipped with, and the quality of its performance has influence on the quality of electronic product to a great extent.Almost each electronic equipment all be unable to do without PCB, littlely arrives greatly Aero-Space, military armament systems etc. to accutron, counter, all comprise of all kinds, the pcb board of different sizes.In recent years, along with improving constantly of production technology, to ultrathin type, small components, high density, the fast development of thin space direction, this trend has been brought a lot of challenges and difficulty to quality detection work to PCB.Therefore the detection of PCB defective has become a key problem in the PCB manufacture process, is the problem that electronic product manufacturer pays special attention to.
Existing defect of printed circuit board detection method is a lot, use two classes that are broadly divided into of automated optical detection: the one, with reference to relative method, it is with PCB to be detected and Standard PC B point-by point comparison, or the feature that extracts on the feature that extracts on the PCB to be checked and the Standard PC B relatively, any difference all is considered to potential defective.Advantage with reference to relative method is conceptive directly perceived, and circuit is realized simple, and shortcoming is the accurate aligning of requirement PCB to be checked and Standard PC B locus, otherwise the false declaration that detects is alert more; Two right and wrong are with reference to method, it is to detect PCB whether to satisfy design rule, mainly be to carry out the size verification, check namely whether conductor and pad equidimension satisfy the desired width of design standards and gap, any and design rule require not to be inconsistent, and all are considered to potential defective, its advantage is to need not with reference to PCB, thereby it need not to aim at, and shortcoming is to detect the defective that satisfies design size, as losing certain bar wire etc. on the PCB.
Summary of the invention
For the weak point that exists in the above-mentioned technology, what the invention provides a kind of versatility, practical, the calculated amount that branch can be deleted, reduces skeleton search and improve pcb board defect recognition efficient detects the method for defect of printed circuit board based on skeletal extraction and range conversion.
For achieving the above object, the invention provides a kind of method that detects defect of printed circuit board based on skeletal extraction and range conversion, may further comprise the steps:
A, extract single pixel framework by looking into concordance list;
B, deletion skeleton branches;
C, unusually search for apart from codomain, according to abnormal conditions with the pcb board classification of defects.
Wherein, in the described steps A, comprise by judging pixel eight connected domains on every side, do not affect under the prerequisite of connected domain number in assurance, with the concordance list method of looking into of useless point deletion.
Wherein, described B step comprises all end points and the node that passes through to detect first skeleton, and each branch is extracted from skeleton; Adopt again the threshold value track algorithm that skeleton is directly pruned.
Wherein, described threshold value track algorithm step comprises:
Set the initial threshold N of calculating pixel number;
Traversal view picture skeleton diagram is found out all straight line end points, with its preservation;
From each end points, the pixel value of statistics process is the number of pixels n of black successively; If during n＞N, do not run into three point of crossing, think that then this end points does not have unnecessary branch, again from other end points; Otherwise, if during n＜N, find to exist three point of crossing, then former road is returned and the pixel value of the black pixel of original process is set to white.
Wherein, described C step also comprises utilizes eight connected region detection range exceptional values, and concrete steps are as follows:
A, by to skeleton diagram search connected domain, record the transverse and longitudinal coordinate of all pixels on every skeleton;
B, by to binary map search connected domain, record the transverse and longitudinal coordinate of each all pixel of connected region;
Each pixel on c, every skeleton of calculating is to the shortest distance values of the edge coordinate point of this connected region;
Mean distance value on d, every skeleton of calculating;
E, each pixel of comparison are to bee-line d and the mean distance value d ' at edge, if d＜d '+n then is projection; If d＞d '+n then is depression, wherein n represents threshold value;
F, detect the pcb board defective, detect defective after, by to data analysis, often the defect coordinate clustering phenomena can appear, can utilize coordinate close classify a total defective as.
The invention has the beneficial effects as follows: compared with prior art, the method that detects defect of printed circuit board based on skeletal extraction and range conversion provided by the invention, extract single pixel framework by looking into concordance list, looking into concordance list can guarantee under the prerequisite that does not affect the connected domain number, with useless point deletion, can guarantee that the skeleton that extracts is single pixel, versatility and practical; Deletion skeleton branches and unusually search for apart from codomain, can reduce calculation deviation does not affect again speed and the accuracy that skeleton is followed the tracks of, can be with all branches' deletions, reduce the skeleton search calculated amount, reduce and undetected phenomenon occurs, improved defect recognition efficient.
Description of drawings
Fig. 1 is the flow chart of steps that detects the method for defect of printed circuit board based on skeletal extraction and range conversion of the present invention;
Fig. 2 is the process flow diagram of eight connected region detection range exceptional values of the method that detects defect of printed circuit board based on skeletal extraction and range conversion of the present invention;
Fig. 3 is the index chart that detects the method for defect of printed circuit board based on skeletal extraction and range conversion of the present invention;
Fig. 4 is the pixel eight connected domain schematic diagram that detect the method for defect of printed circuit board based on skeletal extraction and range conversion of the present invention;
Fig. 5 is the nine grids figure that detects the method for defect of printed circuit board based on skeletal extraction and range conversion of the present invention.
Embodiment
In order more clearly to explain the present invention, below in conjunction with accompanying drawing the present invention is done to describe further.
See also Fig. 1, the method that detects defect of printed circuit board based on skeletal extraction and range conversion provided by the invention may further comprise the steps:
S1, extract single pixel framework by looking into concordance list;
S2, deletion skeleton branches;
S3, unusually search for apart from codomain, according to abnormal conditions with the pcb board classification of defects.
See also Fig. 3-Fig. 5, in step S1, comprise by judging pixel eight connected domains on every side, do not affect under the prerequisite of connected domain number in assurance, with the concordance list method of looking into of useless point deletion, internal point, breakpoint, isolated point and frontier point can not be deleted in looking into the concordance list method, and the using method of looking into concordance list is as follows: if eight connected domain peripheral regions are black pixel points, think that then being worth is 0, for white is then got value corresponding in the nine grids.For the central point of Fig. 4, be with three white points, white point is mapped in the lattice of Fig. 5 palace, its total value is 1+4+32=37, corresponding to getting the 38 in the concordance list.By that analogy, for getting each black pixel point in the image, can be worth according to eight zone calculating on every side, check that then the respective items in the concordance list determines whether keeping this point.
In the present embodiment, realize the rejecting of skeleton branches, at first by detecting all end points and the node of skeleton, each branch be extracted from skeleton, but need to satisfy three conditions: one, keep the topology of primitive character, namely can not disconnect skeleton; Two, continuous, the subtle change of namely pruning degree only causes the subtle change of skeleton; Three, local, can go to estimate from the local message of skeleton the conspicuousness of this point.Based on above-mentioned three kinds of optimal conditions, the algorithm that this paper adopts threshold value to follow the tracks of is directly pruned skeleton.
Wherein, threshold value track algorithm step comprises: set the initial threshold N traversal view picture skeleton diagram of calculating pixel number, find out all straight line end points, with its preservation; From each end points, the pixel value of statistics process is the number of pixels n of black successively; If during n＞N, do not run into three point of crossing, think that then this end points does not have unnecessary branch, again from other end points; Otherwise, if during n＜N, find to exist three point of crossing, then former road is returned and the pixel value of the black pixel of original process is set to white.Usually initial threshold N gets different value according to the character of research object, this algorithm is got threshold value N=12, adopt unnecessary point deletion principle to process, unnecessary pixel and unnecessary end branch are removed, make skeleton diagram neat and tidy, provide single pixel extraction, the connected curve of one direction code for following the tracks of skeleton, reduce the calculated amount of skeleton search, improve the efficient of defect recognition.
See also Fig. 2, behind the deletion skeleton branches, can obtain the skeleton diagram of a secondary less branch, utilize eight connected region detection range exceptional values, step is as follows:
S31, by to skeleton diagram search connected domain, record the transverse and longitudinal coordinate of all pixels on every skeleton;
S32, by to binary map search connected domain, record the transverse and longitudinal coordinate of each all pixel of connected region;
Each pixel on S33, every skeleton of calculating is to the shortest distance values of the edge coordinate point of this connected region, key is that initial search point is not with 0 beginning, need to set a threshold value, prevent from stopping and start-up portion has and do not delete clean branch at skeleton, cause distance value mistake to occur.The deletion branch after, basically branch very, by a large amount of tests, threshold value is more satisfactory at 10-15;
Mean distance value on S34, every skeleton of calculating arranges that according to the cabling of printed circuit board (PCB) normal, the distance value of each pixel should be close with the mean distance value, if not close, then may be the defective place;
S35, each pixel of comparison are to bee-line d and the mean distance value d ' at edge, if d＜d '+n then is projection; If d＞d '+n then is depression, wherein n represents threshold value, and by a large amount of picture test analysis, this threshold value setting is that 2-3 is more satisfactory;
S36, detect the pcb board defective, detect defective after, by to data analysis, often the defect coordinate clustering phenomena can appear, can utilize coordinate close classify a total defective as, can be so that the defective decreased number, but can not occur undetected.
Advantage of the present invention is:
1, this algorithm is owing to needing the transverse and longitudinal coordinate of all pixels on every skeleton of record, so must guarantee that the skeleton that extracts is single pixel, be different from skeleton that traditional sense extracts and mostly be not each point and be single pixel, its highly versatile and practical.
2, the deletion skeleton branches can cause the calculating of distance value deviation to occur, and can affect speed and accuracy that skeleton is followed the tracks of, therefore must remove the accidental end branch that occurs.From every skeleton end points, utilize eight connected regions to judge whether successively to have triradius in three directions, can be with all branch's deletions, satisfactory for result, the number of defects order is reduced, but undetected, as to reduce skeleton search calculated amount can not occur, improve the efficient of defect recognition.
Above disclosed only be several specific embodiment of the present invention, but the present invention is not limited thereto, the changes that any person skilled in the art can think of all should fall into protection scope of the present invention.
Claims (5)
1. a method that detects defect of printed circuit board based on skeletal extraction and range conversion is characterized in that, may further comprise the steps:
A, extract single pixel framework by looking into concordance list;
B, deletion skeleton branches;
C, unusually search for apart from codomain, according to abnormal conditions with the pcb board classification of defects.
2. the method that detects defect of printed circuit board based on skeletal extraction and range conversion according to claim 1, it is characterized in that, in the described steps A, comprise by judging pixel eight connected domains on every side, do not affect under the prerequisite of connected domain number in assurance, with the concordance list method of looking into of useless point deletion.
3. the method that detects defect of printed circuit board based on skeletal extraction and range conversion according to claim 1 is characterized in that, described B step comprises first and by detecting all end points and the node of skeleton each branch to be extracted from skeleton; Adopt again the threshold value track algorithm that skeleton is directly pruned.
4. the method that detects defect of printed circuit board based on skeletal extraction and range conversion according to claim 3 is characterized in that, described threshold value track algorithm step comprises:
Set the initial threshold N of calculating pixel number;
Traversal view picture skeleton diagram is found out all straight line end points, with its preservation;
From each end points, the pixel value of statistics process is the number of pixels n of black successively; If during n＞N, do not run into three point of crossing, think that then this end points does not have unnecessary branch, again from other end points; Otherwise, if during n＜N, find to exist three point of crossing, then former road is returned and the pixel value of the black pixel of original process is set to white.
5. the method that detects defect of printed circuit board based on skeletal extraction and range conversion according to claim 4 is characterized in that, described C step also comprises utilizes eight connected region detection range exceptional values, and concrete steps are as follows:
A, by to skeleton diagram search connected domain, record the transverse and longitudinal coordinate of all pixels on every skeleton;
B, by to binary map search connected domain, record the transverse and longitudinal coordinate of each all pixel of connected region;
Each pixel on c, every skeleton of calculating is to the shortest distance values of the edge coordinate point of this connected region;
Mean distance value on d, every skeleton of calculating;
E, each pixel of comparison are to bee-line d and the mean distance value d ' at edge, if d＜d '+n then is projection; If d＞d '+n then is depression, wherein n represents threshold value;
F, detect the pcb board defective, detect defective after, by to data analysis, often the defect coordinate clustering phenomena can appear, can utilize coordinate close classify a total defective as.
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Cited By (12)
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| CN108830832A (en) * | 2018-05-15 | 2018-11-16 | 佛山市南海区广工大数控装备协同创新研究院 | A kind of plastic barrel surface defects detection algorithm based on machine vision |
| CN110910359A (en) * | 2019-11-13 | 2020-03-24 | 广东工业大学 | FPC (flexible printed circuit) line detection method based on feature tracking and skeleton feature matching |
| CN112116591A (en) * | 2020-11-18 | 2020-12-22 | 惠州高视科技有限公司 | Method for detecting open circuit of etching circuit |
| CN112579810A (en) * | 2019-09-30 | 2021-03-30 | 深圳市嘉立创科技发展有限公司 | Printed circuit board classification method and device, computer equipment and storage medium |
| CN113838045A (en) * | 2021-09-30 | 2021-12-24 | 佛山市南海区广工大数控装备协同创新研究院 | PCB copper-clad circuit skeleton contour extraction method for improving spur removal algorithm |
| CN114384204A (en) * | 2021-12-07 | 2022-04-22 | 广州兴森快捷电路科技有限公司 | PCB jointed board detection device, system, method and storage medium |
| CN114842275A (en) * | 2022-07-06 | 2022-08-02 | 成都数之联科技股份有限公司 | Circuit board defect judging method, training method, device, equipment and storage medium |
| CN114926417A (en) * | 2022-05-09 | 2022-08-19 | 华南理工大学 | Microscopic imaging detection method and system for weak scratches on surface of high-density flexible FPC |
| CN115082428A (en) * | 2022-07-20 | 2022-09-20 | 江苏茂融智能科技有限公司 | Metal spot detection method and system based on neural network |
| CN115272320A (en) * | 2022-09-27 | 2022-11-01 | 江苏昱恒电气有限公司 | Method for detecting surface printing quality of electronic product |
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| CN108830832A (en) * | 2018-05-15 | 2018-11-16 | 佛山市南海区广工大数控装备协同创新研究院 | A kind of plastic barrel surface defects detection algorithm based on machine vision |
| CN112579810B (en) * | 2019-09-30 | 2023-10-27 | 深圳市嘉立创科技发展有限公司 | Printed circuit board classification method, device, computer equipment and storage medium |
| CN112579810A (en) * | 2019-09-30 | 2021-03-30 | 深圳市嘉立创科技发展有限公司 | Printed circuit board classification method and device, computer equipment and storage medium |
| CN110910359A (en) * | 2019-11-13 | 2020-03-24 | 广东工业大学 | FPC (flexible printed circuit) line detection method based on feature tracking and skeleton feature matching |
| CN110910359B (en) * | 2019-11-13 | 2024-03-19 | 广东工业大学 | FPC circuit detection method based on feature tracking and skeleton feature matching |
| CN112116591A (en) * | 2020-11-18 | 2020-12-22 | 惠州高视科技有限公司 | Method for detecting open circuit of etching circuit |
| CN112116591B (en) * | 2020-11-18 | 2021-03-30 | 惠州高视科技有限公司 | Method for detecting open circuit of etching circuit |
| CN113838045B (en) * | 2021-09-30 | 2024-02-02 | 佛山市南海区广工大数控装备协同创新研究院 | PCB copper-clad circuit skeleton contour extraction method for improving bone spur removal algorithm |
| CN113838045A (en) * | 2021-09-30 | 2021-12-24 | 佛山市南海区广工大数控装备协同创新研究院 | PCB copper-clad circuit skeleton contour extraction method for improving spur removal algorithm |
| CN114384204A (en) * | 2021-12-07 | 2022-04-22 | 广州兴森快捷电路科技有限公司 | PCB jointed board detection device, system, method and storage medium |
| CN114384204B (en) * | 2021-12-07 | 2024-03-22 | 广州兴森快捷电路科技有限公司 | PCB jointed board detection device, system, method and storage medium |
| CN114926417A (en) * | 2022-05-09 | 2022-08-19 | 华南理工大学 | Microscopic imaging detection method and system for weak scratches on surface of high-density flexible FPC |
| CN114926417B (en) * | 2022-05-09 | 2024-05-28 | 华南理工大学 | Microscopic imaging detection method and system for weak scratches on surface of high-density flexible FPC |
| CN114842275A (en) * | 2022-07-06 | 2022-08-02 | 成都数之联科技股份有限公司 | Circuit board defect judging method, training method, device, equipment and storage medium |
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| CN115272320A (en) * | 2022-09-27 | 2022-11-01 | 江苏昱恒电气有限公司 | Method for detecting surface printing quality of electronic product |
| CN115272320B (en) * | 2022-09-27 | 2022-12-13 | 江苏昱恒电气有限公司 | Method for detecting surface printing quality of electronic product |
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| CN116228634B (en) * | 2022-12-07 | 2023-12-22 | 辉羲智能科技(上海)有限公司 | Distance transformation calculation method, application, terminal and medium for image detection |
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