CN112330678A - Product edge defect detection method - Google Patents

Product edge defect detection method Download PDF

Info

Publication number
CN112330678A
CN112330678A CN202110015305.XA CN202110015305A CN112330678A CN 112330678 A CN112330678 A CN 112330678A CN 202110015305 A CN202110015305 A CN 202110015305A CN 112330678 A CN112330678 A CN 112330678A
Authority
CN
China
Prior art keywords
template
edge
edge information
detected
small
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110015305.XA
Other languages
Chinese (zh)
Other versions
CN112330678B (en
Inventor
胡昌欣
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.)
Casi Vision Technology Luoyang Co Ltd
Casi Vision Technology Beijing Co Ltd
Original Assignee
Casi Vision Technology Luoyang Co Ltd
Casi Vision Technology Beijing Co Ltd
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 Casi Vision Technology Luoyang Co Ltd, Casi Vision Technology Beijing Co Ltd filed Critical Casi Vision Technology Luoyang Co Ltd
Priority to CN202110015305.XA priority Critical patent/CN112330678B/en
Publication of CN112330678A publication Critical patent/CN112330678A/en
Application granted granted Critical
Publication of CN112330678B publication Critical patent/CN112330678B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/02—Affine transformations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Quality & Reliability (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

The application discloses a product edge defect detection method. The method comprises the following steps: step 1, inputting an image; step 2, preprocessing an image; step 3, coarse matching of templates; step 4, segmenting the edge information to be detected; step 5, establishing a template data structure; step 6, down-sampling the edge information to be detected; step 7, matching in the template; step 8, calculating affine transformation parameters; step 9, template fine matching; step 10 edge defect detection. The method adopts the edge information of the binary image, is insensitive to illumination change and edge gray scale, and has the advantages of stability and reliability; the template matching parameters are iterated from coarse to fine by utilizing edge information down sampling, so that the calculation speed is high; the precise matching mode based on the RANSAC principle and the iterative optimization of parameters has the advantage of high detection precision; the template matching mode of the sectional matching has the advantage of tensile resistance.

Description

Product edge defect detection method
Technical Field
The invention relates to a defect detection method for cover plates and plate products, in particular to a rapid stretch-resistant edge defect detection method.
Background
Various shapes of edges are often included in industrial products, such as circular, rectangular, and a combination of straight and circular arcs, among others. The edge is an important characteristic of industrial products, the quality of the industrial products is directly influenced, and the defects of the edge of the common products are mostly serious defects of the products, such as bud defects, saw teeth, edge breakage and the like, which directly cause the product to be scrapped. Edge defect detection of a product is an important step for ensuring the quality of the product.
The edge defect detection mainly detects the consistency of the edge of the industrial product, namely detects whether the edge of the industrial product has defects such as convex marks, dents and the like. Specifically, for example, edge defect detection is performed on the mobile phone outer screen and the integrated circuit silicon wafer, the mobile phone outer screen and the integrated circuit silicon wafer generally have regular edges, the mobile phone outer screen has a rectangular edge, and the integrated circuit silicon wafer has a combined edge of an arc and a straight line.
If the edge of the mobile phone outer screen has defects, the assembly of the mobile phone outer screen is influenced, and even the mobile phone cannot be normally used; if the edge of the integrated circuit silicon wafer has defects, the manufacturing of the integrated circuit is affected, and even the quality of the chip based on the integrated circuit silicon wafer is reduced. Therefore, edge defect detection of industrial products is an important link in industrial production.
In the prior art, some edge defect detection methods are manual detection. A worker generally inspects an image of an object to be detected in a visual inspection mode, and checks whether a dent or a convex defect exists, however, the detection mode depends on visual inspection of human eyes, the precision is difficult to guarantee, and visual fatigue is easily caused by long-time inspection, so that the detection efficiency is reduced, and even wrong detection and missed detection are caused.
In the prior art, some methods adopt computer vision edge defect detection to detect. The research universality aiming at the edge defect detection algorithm at home and abroad is not strong, one type of algorithm adopts a straight line fitting mode to compare the distance from an edge point to a fitting straight line to detect the edge defect of a straight line region; one type of algorithm compares the change of the front area and the rear area by adopting a switching operation mode to detect the edge defect of a complete area with smooth edge; and the other algorithm adopts a template matching mode to compare the edge defects of the different detection target images to be detected and the template.
However, the above methods are not suitable for defect detection of stretch type edges.
Disclosure of Invention
The invention aims to solve the technical problem that a computer vision detection method in the prior art is not suitable for detecting defects of stretching edges, and provides a product edge defect detection method. The detection method comprises the steps of obtaining affine transformation parameters according to rough matching of minimum circumscribed rectangle features of edge points, calculating the similarity of a template and a to-be-detected object from rough to fine according to a similarity measurement function and by using edge information of different down-sampling scales, updating the affine transformation parameters, updating the template and the to-be-detected pixel-level affine transformation parameters again by using RANSAC and the similarity measurement function, optimizing sub-pixel-level affine transformation parameters corresponding to each segment under the target updating precision by overlapping the radiation transformation parameters near the pixel-level radial transformation parameters by using segmented to-be-detected edge information, and realizing edge defect detection of edge stretching products by using a template matching mode of each segment of affine transformation parameters and segment matching.
In order to solve the technical problem, the invention provides a product edge defect detection method, which comprises the following steps:
step 1, image input: the method comprises the steps of (1) including a template small picture and a target image;
step 2, image preprocessing: determining a to-be-detected area of a target image to obtain a to-be-detected small image; preprocessing an image to acquire image edge information; adjusting the edge information to enable the template edge information to correspond to the edge information to be detected;
step 3, coarse template matching: calculating the coarse matching of the minimum circumscribed rectangle features of the edge points to obtain affine transformation parameters;
step 4, segmenting the edge information to be detected: cutting edge information of the small graph to be detected into a plurality of equal parts which are partially overlapped;
step 5, creating a template data structure: creating edge information of the template small graph into a data structure of KD-Tree (k-dimensional Tree);
step 6, edge information to be detected is subjected to down-sampling: acquiring edge information of each section of the small graph to be detected with different down-sampling scales;
and 7, matching in the template: calculating edge information of each section of the small graph to be detected under the template rough matching affine transformation parameters; calculating the similarity between each section of edge information of the small graph to be detected and the edge information of the template small graph and updating corresponding affine transformation parameters;
step 8, affine transformation parameters are calculated: calculating edge information of each section of the small graph to be detected under the matched affine transformation parameters in the template; calculating the similarity between each section of edge information of the small graph to be detected and the edge information of the template small graph under random consistent sampling and updating affine transformation parameters corresponding to each section of edge information of the small graph to be detected;
step 9, template fine matching: calculating edge information of each section of the small graph to be detected under random consistency sampling affine transformation parameters; performing iterative optimization near the pixel-level radial transformation parameters to update affine transformation parameters corresponding to edge information of each section of the small graph to be detected under the target precision;
step 10, edge defect detection: calculating edge information of each section of the small graph to be detected under the condition that the template is matched with the affine transformation parameters accurately; and searching the edge information of the corresponding template small graph to realize the detection of the edge defect in the to-be-detected area of the target image.
Preferably, all edges or partial edge regions in the image are selected as template minimaps using one or more tools of rectangle, circle, polygon.
Preferably, the determining the coordinate transformation in the suspected region of the target image further comprises: and mapping the ROI coordinates under the connected coordinate system to the region to be detected.
Preferably, the preprocessing the image further comprises: and carrying out binarization on the template small image and the small image to be detected by adopting fixed threshold binarization or adaptive threshold binarization, wherein the edge information comprises edge point coordinates and a gradient direction.
Preferably, the adjusting the edge information further comprises: screening and sorting contours, eliminating image boundary contour points, adjusting contour sequence to make contours continuous, and determining the corresponding relation between a template and edge information to be detected.
Preferably, the template coarse matching further comprises the following steps:
a. respectively solving the minimum external rectangle of the edge points of the template small graph and the small graph to be detected, and the coordinates of the center point and the deflection angle characteristics of the minimum external rectangle;
b. calculating a rough matching radiation transformation parameter by using the coordinate of the central point of the minimum circumscribed rectangle and the deflection angle;
c. and updating the edge information of the small graph of the template through affine transformation parameters.
Preferably, the cutting mode for cutting the edge information to be measured is equal division of partial coincidence.
Preferably, the method for making the template thumbnail edge information template in the matching of the templates further comprises: and creating the edge information of the template small graph into a KD-Tree data structure.
Preferably, edge information corresponding to the maximum similarity measurement function is determined by using a random consistency sampling principle, so as to update affine transformation parameters under the current precision.
Preferably, the emission transformation parameters are iteratively optimized to update the emission transformation parameters at sub-pixel precision in the vicinity of the pixel-precision emission transformation parameters.
Preferably, the method for detecting the edge defects in the suspected region of the target image comprises the following steps:
a. mapping each section of edge information of the small picture to be detected to a template small picture coordinate system by using affine transformation parameters corresponding to each section of edge information of the small picture to be detected;
b. matching the edge of the small graph to be detected and the edge of the template small graph in a segmentation manner to find abnormal edge points of the small graph to be detected;
c. clustering and growing abnormal points and removing out-of-range abnormal points;
d. completing the defects and calculating the defect characteristics;
e. merging coincident defects and features.
Preferably, the down-sampling mode further comprises: the segment edge information is downsampled to 1/2,1/4 data size.
The beneficial effects of the invention include: the method is based on the edge information of the product image, an edge information template is manufactured by acquiring the edge information of the small image of the template, the ROI of the target image to be detected is determined by utilizing coordinate transformation to acquire the small image to be detected, the edge information to be detected with different down-sampling scales is manufactured by acquiring the edge information of the small image to be detected, the rapid, stable and real-time online edge defect detection is realized aiming at displacement, rotation, scaling, illumination change, edge stretching and the like of the target image, and the method can be applied to the occasions of performing the edge defect detection in an online or offline state through machine vision.
The rapid stretch-resistant edge defect method based on template matching adopts edge information of a binary image, is insensitive to illumination change and edge gray, and has the advantages of stability and reliability; the template matching parameters are iterated from coarse to fine by utilizing edge information down sampling, so that the calculation speed is high; the precise matching mode which utilizes the RANSAC (random consistent sampling) principle and parameter iterative optimization has the advantage of high detection precision; the template matching mode of the sectional matching has the advantage of tensile resistance.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only a part of the embodiments or prior art, and other similar or related drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a product defect detection method according to an embodiment of the present invention (top);
FIG. 2 is a flow chart of a product defect detection method according to an embodiment of the present invention (below);
FIG. 3 is a small drawing of a framed template according to an embodiment of the present invention; wherein, the frame selection part 301, the target image space 302;
FIG. 4 is a diagram of mapping ROI coordinates to a region to be inspected in the connected coordinate system according to the embodiment of the present invention; the system comprises a ROI coordinate mapping 401, a part to be detected 402, a connected coordinate system XOY 403 and a target image space 404 under the connected coordinate system;
FIG. 5 is a diagram of edge information of a small graph of a template according to an embodiment of the present invention; wherein, the edge point coordinates and gradient direction 501 of the template small graph;
FIG. 6 is a diagram illustrating minimum circumscribed rectangles of edge points of a small graph to be measured according to an embodiment of the present invention; the minimum external rectangle center point coordinate 601 of the edge point of the small graph to be detected and the minimum external rectangle center point deflection angle 602 of the edge point of the small graph to be detected are included.
FIG. 7 is a diagram of partially overlapping and dividing edge information of a small graph to be measured into a plurality of equal parts according to an embodiment of the present invention; the edge information of the small graph to be detected is partially overlapped and divided into equal parts 701;
FIG. 8 is a graph of edge information of different down-sampling scales of the small graph to be measured according to the embodiment of the present invention; the method comprises the following steps that original outline information 801 of a small graph to be tested, downsampling outline information 802 of the small graph 1/2 to be tested, and downsampling outline information 803 of the small graph 1/4 to be tested;
fig. 9 is a flowchart of an affine transformation parameter updating strategy according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to examples. The present invention will be described in further detail below to make the objects, aspects and advantages of the present invention clearer and more clear, but the present invention is not limited to these examples.
The invention provides a rapid stretch-resistant edge defect detection method based on template matching. The method comprises the steps of preparing an edge information template by obtaining edge information of a template small picture based on image edge information, determining a target image region to be detected ROI by coordinate transformation to obtain a small picture to be detected, preparing edge information to be detected with different down-sampling scales by obtaining the edge information of the small picture to be detected, obtaining affine transformation parameters according to the minimum external rectangle feature rough matching of edge points, roughly and precisely calculating the similarity between the template and the region to be detected and updating the affine transformation parameters according to a similarity measurement function and by using the edge information with different down-sampling scales, updating the template and the pixel level affine transformation parameters to be detected again by using RANSAC (random consistent sampling) and the similarity measurement function, optimizing and updating sub-pixel level affine transformation parameters corresponding to each section under target precision by using the segmented edge information to be detected to superpose the radiation transformation parameters near the pixel level radial transformation parameters, and realizing the edge information in the target image region to be detected region by using the template matching mode of each section of affine transformation parameters and and detecting defects.
The method can realize rapid, stable and real-time online edge defect detection aiming at displacement, rotation, scaling, illumination change, edge stretching and the like of the target image, and can be applied to the occasions of performing the edge defect detection in an online or offline state through machine vision.
The invention discloses a product edge defect detection method, which comprises the following steps:
step 1, image input: the method comprises the steps of (1) including a template small picture and a target image;
step 2, image preprocessing: determining a to-be-detected area of a target image to obtain a to-be-detected small image; preprocessing an image to acquire image edge information; adjusting the edge information to enable the template edge information to correspond to the edge information to be detected;
step 3, coarse template matching: calculating the coarse matching of the minimum circumscribed rectangle features of the edge points to obtain affine transformation parameters;
step 4, segmenting the edge information to be detected: cutting edge information of the small graph to be detected into a plurality of equal parts which are partially overlapped;
step 5, creating a template data structure: creating edge information of the template small graph into a data structure of KD-Tree (k-dimensional Tree);
step 6, edge information to be detected is subjected to down-sampling: acquiring edge information of each section of the small graph to be detected with different down-sampling scales;
and 7, matching in the template: calculating edge information of each section of the small graph to be detected under the template rough matching affine transformation parameters; calculating the similarity between each section of edge information of the small graph to be detected and the edge information of the template small graph and updating corresponding affine transformation parameters;
step 8, affine transformation parameters are calculated: calculating edge information of each section of the small graph to be detected under the matched affine transformation parameters in the template; calculating the similarity between each section of edge information of the small graph to be detected and the edge information of the template small graph under random consistent sampling and updating affine transformation parameters corresponding to each section of edge information of the small graph to be detected;
step 9, template fine matching: calculating edge information of each section of the small graph to be detected under random consistency sampling affine transformation parameters; performing iterative optimization near the pixel-level radial transformation parameters to update affine transformation parameters corresponding to edge information of each section of the small graph to be detected under the target precision;
step 10, edge defect detection: calculating edge information of each section of the small graph to be detected under the condition that the template is matched with the affine transformation parameters accurately; and searching the edge information of the corresponding template small graph to realize the detection of the edge defect in the to-be-detected area of the target image.
Preferably, all or part of the edge regions in the image which are obvious are selected as the template thumbnail by using any one of the tools of rectangle, circle, polygon and the like.
Preferably, the coordinate transformation is to map the ROI coordinates under a connected coordinate system to the region to be detected.
Preferably, the preprocessing is to binarize the template small graph and the small graph to be detected by using fixed threshold binarization or adaptive threshold binarization, and the edge information includes edge point coordinates and gradient directions.
Preferably, the adjusting of the edge information includes contour screening and sorting, image boundary contour point elimination, contour sequence adjustment to make the contours continuous, and determination of the corresponding relationship between the template and the edge information to be measured by using an icp (iterative closed point) algorithm.
Preferably, the coarse matching employs the following steps: a. respectively solving the minimum external rectangle of the edge points of the template small graph and the small graph to be detected, and the coordinates of the center point and the deflection angle characteristics of the minimum external rectangle; b. calculating a rough matching radiation transformation parameter by using the coordinate of the central point of the minimum circumscribed rectangle and the deflection angle; c. and updating the edge information of the small graph of the template through affine transformation parameters.
Preferably, the cutting pattern is a partially overlapping bisection.
Preferably, the template is made by creating edge information of the template thumbnail into a data structure of a KD-Tree (k-dimensional Tree), and the downsampling is performed by downsampling each segment of edge information into 1/2,1/4 data size.
Preferably, the similarity measurement function is the sum of the number of edge points, the distance sum and the sum of gradient direction angles which satisfy a certain condition, and the update affine transformation parameter strategy is that the similarity measurement function under the large-scale down-sampling is larger than the original similarity measurement function, the similarity measurement function under the upper-level down-sampling is calculated until the similarity measurement function under the non-down-sampling is larger than the original similarity measurement function, and the affine transformation parameter is updated.
Preferably, edge information corresponding to the maximum similarity metric function is determined by using a RANSAC (random consistent sampling) principle, so as to update affine transformation parameters at the current precision.
Preferably, the emission transformation parameters are iteratively optimized to update the emission transformation parameters at sub-pixel precision in the vicinity of the pixel-precision emission transformation parameters.
Preferably, the method for detecting the edge defects in the suspected region of the target image comprises the following steps: a. mapping each section of edge information of the small picture to be detected to a template small picture coordinate system by using affine transformation parameters corresponding to each section of edge information of the small picture to be detected; b. matching the edge of the small graph to be detected and the edge of the template small graph in a segmentation manner to find abnormal edge points of the small graph to be detected; c. clustering and growing abnormal points and removing out-of-range abnormal points; d. completing the defects and calculating the defect characteristics; e. merging coincident defects and features.
As shown in fig. 1 and fig. 2, which are flow charts (top and bottom) of the product defect detection method of the present embodiment; fig. 1 and fig. 2 are combined together to form a complete flow chart of the product defect detection method. The specific process comprises the following steps:
step 1, image input: including a template thumbnail
Figure 747021DEST_PATH_IMAGE001
And a target image
Figure 601844DEST_PATH_IMAGE002
；
Step 2, image preprocessing: coordinate mapping is utilized to determine a target image to-be-detected region ROI to obtain a to-be-detected small image
Figure 381582DEST_PATH_IMAGE003
(ii) a Preprocessing an image to obtain image edge information
Figure 776791DEST_PATH_IMAGE004
Figure 30311DEST_PATH_IMAGE005
(ii) a Adjusting edge information to make template edge information
Figure 852774DEST_PATH_IMAGE006
And edge information to be checked
Figure 57490DEST_PATH_IMAGE007
Corresponding;
step 3, coarse template matching: obtaining affine transformation parameters by calculating coarse matching of minimum circumscribed rectangle features of edge points
Figure 256390DEST_PATH_IMAGE008
；
Step 4, segmenting the edge information to be detected: dividing the edge information of the small graph to be detected into a plurality of equal parts which are partially overlapped
Figure 862952DEST_PATH_IMAGE009
；
Step 5, creating a template data structure: creating the edge information of the template small graph into a data structure of KD-Tree (k-dimensional Tree)
Figure 856316DEST_PATH_IMAGE010
；
Step 6, edge information to be detected is down-sampled: obtaining edge information of each section of small graph to be detected with different down-sampling scales
Figure 610645DEST_PATH_IMAGE011
；
And 7, matching in the template: calculating edge information of each section of small graph to be detected under template rough matching affine transformation parameters
Figure 596925DEST_PATH_IMAGE012
(ii) a According to the similarity measurement function, calculating the similarity between each section of edge information of the small graph to be detected and the edge information of the template small graph from coarse to fine according to the edge information of different down-sampling scales
Figure 120310DEST_PATH_IMAGE013
Updating the corresponding affine transformation parameters;
step 8, RANSAC calculates affine transformation parameters:calculating edge information of each section of small graph to be detected under matched affine transformation parameters in template
Figure 487837DEST_PATH_IMAGE014
(ii) a Calculating the similarity between each segment of edge information of the small picture to be detected and the edge information of the template small picture by utilizing RANSAC (random consistent sampling) principle and updating affine transformation parameters corresponding to each segment of edge information of the small picture to be detected under the current precision
Figure 729463DEST_PATH_IMAGE015
；
Step 9, template fine matching: calculating edge information of each section of small graph to be measured under random consistency sampling affine transformation parameters
Figure 437875DEST_PATH_IMAGE016
(ii) a Affine transformation parameters corresponding to edge information of each section of small graph to be tested under the condition of updating target precision by iterative optimization near pixel-level radioactive transformation parameters
Figure 81346DEST_PATH_IMAGE017
；
Step 10, edge defect detection: calculating edge information of each section of the small graph to be detected under the condition that the template is matched with the affine transformation parameters accurately; finding corresponding template small image edge information to realize edge defect detection in target image to-be-detected area
Figure 354195DEST_PATH_IMAGE018
。
The following is a detailed description of the above steps:
step 1: image input
Fig. 3 is a block diagram of a template according to an embodiment of the present invention. Where 301 is the boxed portion and 302 is the target image space. In the embodiment of the invention, the image input is from a linear array camera image sensor in a machine vision system, and an image with a clearer edge and a centered target object is selected as a template image in the input image. Selecting template images in a rectangular, circular, polygonal and other geometric tool box under a connected coordinate system XOYAll or part of the edge area is used as a template small picture
Figure 83117DEST_PATH_IMAGE001
Recording geometric tool coordinates (x, y) under a connected body coordinate system XOY, and taking a subsequent input image as a target image
Figure 99614DEST_PATH_IMAGE002
。
Step 2: image pre-processing
In the embodiment of the present invention, the region to be measured corresponding to the template thumbnail may appear at any position of the target image, but the position of the region to be measured under the connected coordinate system is fixed, so as shown in fig. 4, the present invention maps the geometric tool coordinate (x, y) coordinate of the framed template thumbnail to the coordinate (x ', y') under the connected coordinate system XOY in the target image to determine the region to be measured, and obtains the thumbnail to be measured through (x ', y')/the region to be measured
Figure 597592DEST_PATH_IMAGE003
。
In the embodiment of the invention, although the template small image and the small image to be detected are both small images, the data volume is still large, the template matching is mostly useless data, the edge information of the image is the key and important data of the template matching, and the good template matching can be realized. As shown in FIG. 5, the invention adopts fixed threshold binarization or adaptive threshold binarization to obtain the binarized images of the template small graph and the small graph to be measured, and in order to avoid the edge information inconsistency caused by illumination change, edge stretching and the like, the edge information such as the edge point of the template small graph, the edge point coordinate p '(x', y ') and the gradient direction e' (x ', y') is obtained in the binarized image of the template small graph
Figure 369239DEST_PATH_IMAGE004
Acquiring edge information such as edge points, edge point coordinates p (x, y), gradient direction e (x, y) and the like of the small graph to be detected from the binary image of the small graph to be detected
Figure 257560DEST_PATH_IMAGE005
。
The edge information of the template small picture and the small picture to be detected acquired in the embodiment of the invention has useless information, such as image boundary points, error information, such as false edge points, asymmetric information, such as the non-correspondence between the outline point information of the small picture to be detected and the outline point information of the template small picture, and the like. The invention further adjusts the acquired edge information, comprising contour screening and sorting, eliminating image boundary contour points, adjusting contour sequence to make the contour continuous, determining the corresponding relation between the to-be-detected and template edge information by utilizing ICP (iterative close Point) algorithm, acquiring the adjusted template small picture edge information
Figure 140066DEST_PATH_IMAGE006
And edge information of the small graph to be tested
Figure 492550DEST_PATH_IMAGE007
。
And step 3: coarse template matching
Although the data volume used for template matching in the embodiment of the invention is small enough relative to the data volume of the image pyramid, the running time of the data volume directly used for template matching is still long. As shown in FIG. 6, the affine transformation parameter of the template rough matching is calculated by obtaining the minimum circumscribed rectangle center point coordinate (x, y) and the deflection angle theta of the edge point of the small graph to be detected, the minimum circumscribed rectangle center point coordinate (x ', y ') and the deflection angle theta ' of the edge point of the template small graph, and the scaling ratio k of the minimum circumscribed rectangle long side L ' of the edge point P ' of the template small graph and the minimum circumscribed rectangle long side L of the edge point P of the small graph to be detected
Figure 871316DEST_PATH_IMAGE008
. The calculation formula of the template rough matching affine transformation parameters is as follows:
Figure 309251DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 729868DEST_PATH_IMAGE020
and 4, step 4: segmenting edge information to be measured
In the embodiment of the invention, the image edge has certain stretching, the template matching mode of integral matching is adopted for integral matching to be optimal, but misdetection of final edge defects can be caused by the matching occurrence deviation of partial positions. As shown in FIG. 7, the invention cuts the outline information of the small graph to be measured by adopting a partially overlapped cutting mode
Figure 140120DEST_PATH_IMAGE009
Several equal parts { P1，P2，…，PnAnd calculating affine transformation parameters of all the segments by using a template matching mode of segment matching to avoid segmentation to defects.
And 5: creating template data structures
In the embodiment of the invention, in order to realize the quick search and matching from the edge information of the small graph to be detected to the edge information of the template small graph, the edge information of the template small graph is created into a data structure of KD-Tree (k-dimensional Tree)
Figure 253570DEST_PATH_IMAGE012
。
Step 6: edge information downsampling to be measured
Fig. 8 is a diagram of edge information of different down-sampling scales of a small graph to be measured according to an embodiment of the present invention. The original outline information 801 of the small graph to be tested, the downsampling outline information 802 of the small graph 1/2 to be tested, and the downsampling outline information 803 of the small graph 1/4 to be tested. In the embodiment of the invention, in order to quickly realize the iterative optimization of affine transformation parameters from coarse to fine and reduce the data volume, the edge point P of the small graph to be detected is down-sampled by P1/2And P1/4Data volume
Figure 116484DEST_PATH_IMAGE011
。
And 7: match in template
In the embodiment of the invention, a template matching mode of segmentation matching is adopted to calculate the similarity measurement function S { SN, SD, S theta } of each segment
Figure 340792DEST_PATH_IMAGE010
Updating affine transformation parameters segment by using affine transformation parameter updating strategy
Figure 402289DEST_PATH_IMAGE021
Figure 624323DEST_PATH_IMAGE013
As shown in fig. 9, the specific implementation steps are as follows:
a. calculating template rough matching affine transformation parameter T0Current segment edge information coordinate P of lower to-be-detected small picture0(x, y) and gradient direction e0（x，y）。
b. Searching edge information coordinate P of corresponding template small graph0' (x ', y ') and gradient direction e0’（x’，y’）。
c. Calculating a similarity measure function S for a current segment0{SN0，SD0，Sθ0}：
Figure 36849DEST_PATH_IMAGE022
Wherein, SN0Is a coordinate P0(x, y) to the coordinate P0' (x ', y ') corresponding coordinate points having a distance less than distance0The number of edge points; SD0Is a coordinate P0(x, y) to the coordinate P0' (x ', y ') corresponding coordinate points having a distance less than distance0The sum of the distances of (a); s theta0Is a coordinate P0(x, y) to the coordinate P0' (x ', y ') corresponding coordinate points having a distance less than distance0The sum of the included angles in the gradient directions.
d. Coarse matching of affine transformation parameters [ theta ] in a template0，x0，y0Nearby with a large step size [ Delta theta ]1，Δx1，Δy1Calculating edge point P by nested loop1/4、P1/2And P1/1S similarity metric function of1/4、S1/2And S1/1If { S }1/4、S1/2And S1/1Are all greater than S0Then update S0Recording the corresponding affine transformation parameters [ theta ]0’，x0’，y0' finding the affine transformation parameter theta corresponding to the maximum similarity metric function01，x01，y01Updating template matching affine transformation parameters
Figure 64848DEST_PATH_IMAGE014
：
T1=T01*T0
Wherein the content of the first and second substances,
Figure 872529DEST_PATH_IMAGE023
and 8: RANSAC for calculating affine transformation parameters
In the embodiment of the invention, the RANSAC (random consistent sampling) principle is utilized to solve the problem that the error exists in updating the template matching affine transformation parameters due to the possible existence of edge defects or interference information and the like in template matching
Figure 999885DEST_PATH_IMAGE015
The method comprises the following specific implementation steps:
a. calculating template matching affine transformation parameter T1Current segment edge information coordinate P of lower to-be-detected small picture1(x, y) and gradient direction e1（x，y）。
b. Searching edge information coordinate P of corresponding template small graph1' (x ', y ') and gradient direction e1’（x’，y’）。
c. Calculating a similarity measure function S for a current segment1{SN1，SD1，Sθ1}：
Figure 899708DEST_PATH_IMAGE024
Wherein, SN1Is a coordinate P1(x, y) to the coordinate P1' (x ', y ') corresponding coordinate points having a distance less than distance1The number of edge points; SD1Is a coordinate P1(x, y) to the coordinate P1' (x ', y ') corresponding coordinate points having a distance less than distance1The sum of the distances of (a); s theta1Is a coordinate P1(x, y) to the coordinate P1' (x ', y ') corresponding coordinate points having a distance less than distance1The sum of the included angles in the gradient directions.
d. Randomly selecting the edge information coordinate P of the current segment of the small picture to be measured1Three points in (x, y) { P1，P2， P3And the coordinates P1' (x, y) corresponding point { P }1’，P2’， P3’}。
Wherein, { P1，P2， P3Is from the coordinate P1(x, y) different parts are randomly selected, and the area of a triangle formed by the three points meets a certain condition.
e. And (3) solving a linear equation set by Gaussian elimination to obtain affine transformation parameters:
AT=B*XT
wherein the content of the first and second substances,
Figure 465819DEST_PATH_IMAGE025
obtaining affine transformation parameters
Figure 705170DEST_PATH_IMAGE026
f. Calculating affine transformation parameters T12Next current segment edge point P1/4、P1/2And P1/1Similarity metric function of { S12 }1/4，S121/2，S121/1If { S12 }1/4，S121/2，S121/1Are all greater than S1Then update S1Recording the corresponding affine transformation parameters T12. Until finding out the similarity measurement function meeting the condition or reaching the maximum affine transformation parameter T corresponding to the similarity measurement function when the random consistency sampling times are reached12。
g. Updating affine transformation parameters
Figure 65745DEST_PATH_IMAGE016
：
T2=T12*T1
And step 9: template fine matching
Fig. 9 is a flowchart illustrating the strategy for updating affine transformation parameters according to the embodiment of the present invention. In the embodiment of the invention, a template matching mode of subsection fine matching is adopted, the similarity measurement function S { SN, SD and S theta } of each section is calculated, and the affine transformation parameter T is updated section by utilizing an affine transformation parameter updating strategy
Figure 452864DEST_PATH_IMAGE017
The method comprises the following specific implementation steps:
a. calculating random consistency sampling affine transformation parameter T2Current segment edge information coordinate P of lower to-be-detected small picture2(x, y) and gradient direction e2（x，y）。
b. Searching edge information coordinate P of corresponding template small graph2' (x ', y ') and gradient direction e2’（x’，y’）。
c. Calculating a similarity measure function S for a current segment2{SN2，SD2，Sθ2}：
Figure 255954DEST_PATH_IMAGE027
Wherein, SN2Is a coordinate P2(x, y) to the coordinate P2' (x ', y ') corresponding coordinate points having a distance less than distance2The number of edge points; SD2Is a coordinate P2(x, y) to the coordinate P2' (x ', y ') corresponding coordinate points having a distance less than distance2The sum of the distances of (a); s theta2Is a coordinate P2(x, y) to the coordinate P2' (x ', y ') corresponding coordinate points having a distance less than distance2The sum of the included angles in the gradient directions.
d. Sampling affine transformation parameters [ theta ] at random consistency2，x2，y2A small step size [ Delta theta ] is adopted around2，Δx2，Δy2Nested loop computation edge point P21/4、P21/2And P21/1Similarity metric function of { S2 }1/4，S21/2，S21/1If { S2 }1/4，S21/2，S21/1Are all greater than S2Then update S2Recording the corresponding affine transformation parameters [ theta ]2’，x2’，y2' finding the affine transformation parameter theta corresponding to the maximum similarity metric function23，x23，y23Updating template matching affine transformation parameters:
T3=T23*T2
wherein the content of the first and second substances,
Figure 42423DEST_PATH_IMAGE028
step 10: edge defect detection
In the embodiment of the invention, the edge defect detection is realized by adopting a template matching mode of sectional matching
Figure 777161DEST_PATH_IMAGE018
The method comprises the following specific implementation steps:
a. calculating template fine matching affine transformation parameter T3Current segment edge information coordinate P of lower to-be-detected small picture3（x，y）。
b. Searching edge information coordinate P of corresponding template small graph3’（x’，y’）。
c. Calculating the average distance deviation:
Figure 385997DEST_PATH_IMAGE029
wherein n is a number satisfying
Figure 559489DEST_PATH_IMAGE030
The number of the edge points of (a),
Figure 6389DEST_PATH_IMAGE031
distance, of point p to point p3Is a fixed value.
d. Finding satisfaction conditions
Figure 912028DEST_PATH_IMAGE032
The abnormal edge point of (1), wherein distance4Is a fixed value.
e. Cluster growth satisfies the condition
Figure 945843DEST_PATH_IMAGE033
And eliminating the boundary-crossing abnormal edge points.
f. And completing the information of the defect abnormal points, and calculating the defect characteristics such as length, width, area, coordinates and the like of the defect.
g. Merge satisfaction condition
Figure 657447DEST_PATH_IMAGE034
Defect d of1And defect d2And outputting the edge defect detection result.
Although the present invention has been described with reference to a few embodiments, it should be understood that the present invention is not limited to the above embodiments, but rather, the present invention is not limited to the above embodiments, and those skilled in the art can make various changes and modifications without departing from the scope of the invention.

Claims (12)

1. A method for detecting edge defects of a product is characterized by comprising the following steps:
step 1, image input: the method comprises the steps of (1) including a template small picture and a target image;
step 2, image preprocessing: determining a to-be-detected area of a target image to obtain a to-be-detected small image; preprocessing an image to acquire image edge information; adjusting the edge information to enable the template edge information to correspond to the edge information to be detected;
step 3, coarse template matching: calculating the coarse matching of the minimum circumscribed rectangle features of the edge points to obtain affine transformation parameters;
step 4, segmenting the edge information to be detected: cutting edge information of the small graph to be detected into a plurality of equal parts which are partially overlapped;
step 5, creating a template data structure: creating edge information of the template small graph into a data structure of the KD-Tree;
step 6, edge information to be detected is subjected to down-sampling: acquiring edge information of each section of the small graph to be detected with different down-sampling scales;
and 7, matching in the template: calculating edge information of each section of the small graph to be detected under the template rough matching affine transformation parameters; calculating the similarity between each section of edge information of the small graph to be detected and the edge information of the template small graph and updating corresponding affine transformation parameters;
step 8, affine transformation parameters are calculated: calculating edge information of each section of the small graph to be detected under the matched affine transformation parameters in the template; calculating the similarity between each section of edge information of the small graph to be detected and the edge information of the template small graph under random consistent sampling and updating affine transformation parameters corresponding to each section of edge information of the small graph to be detected;
step 9, template fine matching: calculating edge information of each section of the small graph to be detected under random consistency sampling affine transformation parameters; performing iterative optimization near the pixel-level radial transformation parameters to update affine transformation parameters corresponding to edge information of each section of the small graph to be detected under the target precision;
step 10, edge defect detection: calculating edge information of each section of the small graph to be detected under the condition that the template is matched with the affine transformation parameters accurately; and searching the edge information of the corresponding template small graph to realize the detection of the edge defect in the to-be-detected area of the target image.
2. The method for detecting edge defects of products according to claim 1, wherein all edges or part of edge regions in the images are selected as template minimaps by using one or more tools of rectangles, circles and polygons.
3. The method of claim 1, wherein determining the coordinate transformation in the suspected area of the target image further comprises: and mapping the ROI coordinates under the connected coordinate system to the region to be detected.
4. The method of claim 1, wherein the preprocessing the image further comprises: and carrying out binarization on the template small image and the small image to be detected by adopting fixed threshold binarization or adaptive threshold binarization, wherein the edge information comprises edge point coordinates and a gradient direction.
5. The method of claim 1, wherein the adjusting the edge information further comprises: screening and sorting contours, eliminating image boundary contour points, adjusting contour sequence to make contours continuous, and determining the corresponding relation between a template and edge information to be detected.
6. The method of claim 1, wherein the rough template matching further comprises:
a. respectively solving the minimum external rectangle of the edge points of the template small graph and the small graph to be detected, and the coordinates of the center point and the deflection angle characteristics of the minimum external rectangle;
b. calculating a rough matching radiation transformation parameter by using the coordinate of the central point of the minimum circumscribed rectangle and the deflection angle;
c. and updating the edge information of the small graph of the template through affine transformation parameters.
7. The method for detecting the edge defect of the product as claimed in claim 1, wherein the dividing manner for dividing the edge information to be detected is equal division of partial coincidence.
8. The method for detecting edge defects of products according to claim 1, wherein the manner of making the template small-image edge information template in the matching of the templates further comprises: and creating the edge information of the template small graph into a KD-Tree data structure.
9. The product edge defect detection method of claim 1, wherein edge information corresponding to a maximum similarity metric function is determined by using a random consistency sampling principle, so as to update affine transformation parameters at the current precision.
10. The method of claim 1, wherein the iterative radial transformation parameter optimization updates the transformation parameters at sub-pixel accuracy near the pixel accuracy radial transformation parameters.
11. The method for detecting the edge defects of the products as claimed in claim 1, wherein the step of detecting the edge defects in the inspected area of the target image comprises the following steps:
a. mapping each section of edge information of the small picture to be detected to a template small picture coordinate system by using affine transformation parameters corresponding to each section of edge information of the small picture to be detected;
b. matching the edge of the small graph to be detected and the edge of the template small graph in a segmentation manner to find abnormal edge points of the small graph to be detected;
c. clustering and growing abnormal points and removing out-of-range abnormal points;
d. completing the defects and calculating the defect characteristics;
e. merging coincident defects and features.
12. The method of claim 1, wherein the down-sampling further comprises: the segment edge information is downsampled to 1/2,1/4 data size.
CN202110015305.XA 2021-01-07 2021-01-07 Product edge defect detection method Active CN112330678B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110015305.XA CN112330678B (en) 2021-01-07 2021-01-07 Product edge defect detection method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110015305.XA CN112330678B (en) 2021-01-07 2021-01-07 Product edge defect detection method

Publications (2)

Publication Number Publication Date
CN112330678A true CN112330678A (en) 2021-02-05
CN112330678B CN112330678B (en) 2021-04-02

Family

ID=74301939

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110015305.XA Active CN112330678B (en) 2021-01-07 2021-01-07 Product edge defect detection method

Country Status (1)

Country Link
CN (1) CN112330678B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112797900A (en) * 2021-04-07 2021-05-14 中科慧远视觉技术(北京)有限公司 Multi-camera plate size measuring method
CN113063731A (en) * 2021-03-24 2021-07-02 上海晨兴希姆通电子科技有限公司 Detection system and detection method for rotary disc type glass cover plate silk screen printing
CN113160161A (en) * 2021-04-14 2021-07-23 歌尔股份有限公司 Method and device for detecting defects at edge of target
CN113237888A (en) * 2021-05-21 2021-08-10 哈尔滨工业大学 Large-caliber optical element damage point online and offline dark field image matching method
CN113409295A (en) * 2021-06-30 2021-09-17 北京兆维电子(集团)有限责任公司 CELL-based edge defect detection method
CN113487561A (en) * 2021-07-02 2021-10-08 成都唐源电气股份有限公司 Pantograph foreign matter detection method and device based on gray gradient abnormal voting

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090245593A1 (en) * 2008-03-31 2009-10-01 Fujitsu Limited Pattern aligning method, verifying method, and verifying device
CN103500448A (en) * 2013-09-25 2014-01-08 华南理工大学 Method for estimating affine transformation model parameters of high density packaging component
CN104134220A (en) * 2014-08-15 2014-11-05 北京东方泰坦科技股份有限公司 Low-altitude remote sensing image high-precision matching method with consistent image space
CN105701473A (en) * 2016-01-15 2016-06-22 深圳市未来媒体技术研究院 Method for matching palm print image detail point characteristics
CN105930858A (en) * 2016-04-06 2016-09-07 吴晓军 Fast high-precision geometric template matching method enabling rotation and scaling functions
CN107543828A (en) * 2017-08-25 2018-01-05 广东工业大学 A kind of Surface Flaw Detection method and system
CN110567965A (en) * 2019-07-31 2019-12-13 华南理工大学 Smartphone glass cover plate edge visual defect detection method
CN111292364A (en) * 2020-01-21 2020-06-16 天目爱视（北京）科技有限公司 Method for rapidly matching images in three-dimensional model construction process
CN111369545A (en) * 2020-03-10 2020-07-03 创新奇智(重庆)科技有限公司 Edge defect detection method, device, model, equipment and readable storage medium
US10732124B2 (en) * 2017-09-12 2020-08-04 Imec Vzw Methods for detecting defects of a lithographic pattern

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090245593A1 (en) * 2008-03-31 2009-10-01 Fujitsu Limited Pattern aligning method, verifying method, and verifying device
CN103500448A (en) * 2013-09-25 2014-01-08 华南理工大学 Method for estimating affine transformation model parameters of high density packaging component
CN104134220A (en) * 2014-08-15 2014-11-05 北京东方泰坦科技股份有限公司 Low-altitude remote sensing image high-precision matching method with consistent image space
CN105701473A (en) * 2016-01-15 2016-06-22 深圳市未来媒体技术研究院 Method for matching palm print image detail point characteristics
CN105930858A (en) * 2016-04-06 2016-09-07 吴晓军 Fast high-precision geometric template matching method enabling rotation and scaling functions
CN107543828A (en) * 2017-08-25 2018-01-05 广东工业大学 A kind of Surface Flaw Detection method and system
US10732124B2 (en) * 2017-09-12 2020-08-04 Imec Vzw Methods for detecting defects of a lithographic pattern
CN110567965A (en) * 2019-07-31 2019-12-13 华南理工大学 Smartphone glass cover plate edge visual defect detection method
CN111292364A (en) * 2020-01-21 2020-06-16 天目爱视（北京）科技有限公司 Method for rapidly matching images in three-dimensional model construction process
CN111369545A (en) * 2020-03-10 2020-07-03 创新奇智(重庆)科技有限公司 Edge defect detection method, device, model, equipment and readable storage medium

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘干: "融合灰度特征与形状特征的图像配准方法", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *
陈栋梁: "机械零件影像边缘检测技术研究及测量系统开发", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113063731A (en) * 2021-03-24 2021-07-02 上海晨兴希姆通电子科技有限公司 Detection system and detection method for rotary disc type glass cover plate silk screen printing
CN113063731B (en) * 2021-03-24 2023-01-20 上海晨兴希姆通电子科技有限公司 Detection system and detection method for rotary disc type glass cover plate silk screen printing
CN112797900A (en) * 2021-04-07 2021-05-14 中科慧远视觉技术(北京)有限公司 Multi-camera plate size measuring method
CN112797900B (en) * 2021-04-07 2021-07-06 中科慧远视觉技术(北京)有限公司 Multi-camera plate size measuring method
CN113160161A (en) * 2021-04-14 2021-07-23 歌尔股份有限公司 Method and device for detecting defects at edge of target
CN113237888A (en) * 2021-05-21 2021-08-10 哈尔滨工业大学 Large-caliber optical element damage point online and offline dark field image matching method
CN113409295A (en) * 2021-06-30 2021-09-17 北京兆维电子(集团)有限责任公司 CELL-based edge defect detection method
CN113409295B (en) * 2021-06-30 2024-03-29 北京兆维电子(集团)有限责任公司 CELL-based edge defect detection method
CN113487561A (en) * 2021-07-02 2021-10-08 成都唐源电气股份有限公司 Pantograph foreign matter detection method and device based on gray gradient abnormal voting
CN113487561B (en) * 2021-07-02 2023-06-30 成都唐源电气股份有限公司 Pantograph foreign matter detection method and device based on gray gradient abnormal voting

Also Published As

Publication number Publication date
CN112330678B (en) 2021-04-02

Similar Documents

Publication Publication Date Title
CN112330678B (en) Product edge defect detection method
US7084968B2 (en) Method for analyzing defect data and inspection apparatus and review system
TWI532116B (en) Method and system for wafer registration
CN115861291B (en) Chip circuit board production defect detection method based on machine vision
CN114693610A (en) Welding seam surface defect detection method, equipment and medium based on machine vision
CN113340909B (en) Glue line defect detection method based on machine vision
CN111539927B (en) Detection method of automobile plastic assembly fastening buckle missing detection device
CN114022483B (en) Injection molding flash area identification method based on edge characteristics
CN114862855B (en) Textile defect detection method and system based on template matching
CN110260818B (en) Electronic connector robust detection method based on binocular vision
US8244042B2 (en) Pattern matching method and computer program for executing pattern matching
CN111861979A (en) Positioning method, positioning equipment and computer readable storage medium
CN116168028B (en) High-speed rail original image processing method and system based on edge filtering under low visibility
CN114972575A (en) Linear fitting algorithm based on contour edge
CN112767399A (en) Semiconductor bonding wire defect detection method, electronic device and storage medium
CN115311279A (en) Machine vision identification method for warp and weft defects of fabric
CN114863492A (en) Method and device for repairing low-quality fingerprint image
CN114549400A (en) Image identification method and device
CN109148433B (en) Method and apparatus for determining dimensions of an integrated circuit device
CN107808165B (en) Infrared image matching method based on SUSAN corner detection
CN111795970B (en) Irregular contour defect detection method
JP2008116206A (en) Apparatus, method, and program for pattern size measurement
JP2005524220A (en) Method for automatically defining partial models
CN116596987A (en) Workpiece three-dimensional size high-precision measurement method based on binocular vision
CN109084721B (en) Method and apparatus for determining a topographical parameter of a target structure in a semiconductor device

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