CN114897864A - Workpiece detection and defect judgment method based on digital and analog information - Google Patents
Workpiece detection and defect judgment method based on digital and analog information Download PDFInfo
- Publication number
- CN114897864A CN114897864A CN202210593470.8A CN202210593470A CN114897864A CN 114897864 A CN114897864 A CN 114897864A CN 202210593470 A CN202210593470 A CN 202210593470A CN 114897864 A CN114897864 A CN 114897864A
- Authority
- CN
- China
- Prior art keywords
- workpiece
- image
- digital
- analog
- gray
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000001514 detection method Methods 0.000 title claims abstract description 28
- 230000007547 defect Effects 0.000 title claims abstract description 22
- 238000013461 design Methods 0.000 claims abstract description 22
- 238000003708 edge detection Methods 0.000 claims abstract description 16
- 230000000007 visual effect Effects 0.000 claims abstract 2
- 230000008569 process Effects 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 230000002950 deficient Effects 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 5
- 230000000877 morphologic effect Effects 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 238000013519 translation Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 3
- 238000012360 testing method Methods 0.000 description 4
- 230000002194 synthesizing effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
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
- G06T7/001—Industrial image inspection using an image reference approach
-
- 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
- G06T3/04—Context-preserving transformations, e.g. by using an importance map
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/181—Segmentation; Edge detection involving edge growing; involving edge linking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/64—Analysis of geometric attributes of convexity or concavity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
- G06T2207/30164—Workpiece; Machine component
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Geometry (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
Description
技术领域technical field
本发明属于图像处理领域,涉及基于数模信息的工件检测及缺陷判断方法。The invention belongs to the field of image processing, and relates to a workpiece detection and defect judgment method based on digital-analog information.
背景技术Background technique
随着计算机科学的进步和工业制造的升级发展,计算机视觉被广泛得应用在工业领域,工业设计软件也在逐步精进功能。在工业制造前一般都会利用工业设计软件如AutoCAD,CATIA等对工件形状进行设计,并对其上一些特征点进行标注。工业设计是对工业产品的造型设计,是为了更好地体现产品特性。设计人员可以根据实际需求,利用设计软件完成产品的设计工作。数模信息主要是用于工件的设计和制造。这有利于在工件制造后,对其相关检测上提供帮助。但目前很少有研究将数模信息和后续图像的处理相结合,提供更多的额外信息。利用数模信息辅助检测是坐标测量技术的新的发展方向。With the advancement of computer science and the upgrading and development of industrial manufacturing, computer vision is widely used in the industrial field, and industrial design software is gradually improving its functions. Before industrial manufacturing, industrial design software such as AutoCAD and CATIA are generally used to design the shape of the workpiece, and mark some feature points on it. Industrial design is the modeling design of industrial products in order to better reflect the product characteristics. Designers can use design software to complete product design work according to actual needs. Digital and analog information is mainly used for the design and manufacture of workpieces. This is beneficial to provide assistance in the relevant inspection of the workpiece after it is manufactured. But few studies have combined digital-analog information with subsequent image processing to provide more additional information. Using digital and analog information to assist detection is a new development direction of coordinate measurement technology.
发明内容SUMMARY OF THE INVENTION
有鉴于此,本发明的目的在于提供一种基于数模信息的工件检测及缺陷判断方法。In view of this, the purpose of the present invention is to provide a workpiece detection and defect judgment method based on digital-analog information.
为达到上述目的,本发明提供如下技术方案:To achieve the above object, the present invention provides the following technical solutions:
基于数模信息的工件检测及缺陷判断方法,该方法包括以下步骤:A workpiece detection and defect judgment method based on digital and analog information, the method includes the following steps:
S1:利用视觉传感器获取完整的工件图像信息;S1: Use the vision sensor to obtain complete workpiece image information;
S2:将所需求的数模信息提取出来,将工件个数、坐标和形态信息保存在对应文件里,并获取合格工件的灰度图,统计其灰度值;S2: Extract the required digital-analog information, save the number of workpieces, coordinates and shape information in the corresponding file, obtain the grayscale map of the qualified workpiece, and count its grayscale values;
S3:针对图像建立对应坐标系,数模坐标信息和图像坐标信息对齐,获取数模坐标系上工件位置在图像上的对应情况;S3: Establish a corresponding coordinate system for the image, align the digital-analog coordinate information with the image coordinate information, and obtain the correspondence of the workpiece position on the digital-analog coordinate system on the image;
S4:根据对齐后图像坐标信息,选择设计对应图像位置框;S4: According to the image coordinate information after alignment, select and design the corresponding image position frame;
S5:将搜索位置对应到图像目标位置框上,在其上对工件进行边缘检测;S5: correspond the search position to the image target position frame, and perform edge detection on the workpiece on it;
S6:利用边缘检测获取目标工件,通过利用工件物体边缘位置和其他位置存在灰度阶跃变化,检测获取图像边缘;S6: Use edge detection to obtain the target workpiece, and detect and obtain the edge of the image by using the edge position of the workpiece object and the gray-scale step change in other positions;
S7:根据实际制造可能产生的误差,确定工件移动的偏差范围;S7: Determine the deviation range of the workpiece movement according to the errors that may occur in actual manufacturing;
S8:对正常形态的待测物图像和存在缺陷的待测物图像分别进行感兴趣区域提取,得到尺寸相同的正常特征图像和缺陷特征图像;S8 : extracting regions of interest for the normal-shaped object-to-be-tested image and the defective-to-be-tested object image, respectively, to obtain a normal feature image and a defect feature image with the same size;
S9:将得到的工件与模板进行匹配对比,确认工件是否满足数模形态信息,确定是否为模板的待检测工件;S9: Match and compare the obtained workpiece with the template, confirm whether the workpiece satisfies the digital-analog shape information, and determine whether it is the workpiece to be detected by the template;
S10:将得到的工件的边缘情况和数模信息对比,判断是否存在工件和工件大小、形态是否满足预先的设计需求;S10: Compare the obtained edge condition of the workpiece with the digital-analog information to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements;
S11:得到工件图像的灰度图,根据预设尺寸在灰度图中建立选区,统计选区内所有像素点的灰度值,计算选区的灰度均值和标准偏差值;S11: Obtain a grayscale image of the workpiece image, establish a selection area in the grayscale image according to a preset size, count the grayscale values of all pixels in the selection area, and calculate the grayscale mean value and standard deviation value of the selection area;
S12:判断工件图像灰度值是否符合标准图像,从而判断工件是否为合格工件,综合模板匹配的结果,将对比得到的结果进行输出;S12: judging whether the gray value of the workpiece image conforms to the standard image, thereby judging whether the workpiece is a qualified workpiece, synthesizing the result of template matching, and outputting the result obtained by comparison;
若部分判断包含灰度值的像素点范围超出标准工件包含灰度值的像素点范围,则认为工件形态过大;If it is partially judged that the range of pixels containing gray values exceeds the range of pixels containing gray values of the standard workpiece, it is considered that the shape of the workpiece is too large;
若部分判断包含灰度值的像素点少于标准工件包含灰度值的像素点范围,则认为工件形态缺损;If it is partially judged that the pixel points containing the gray value are less than the pixel range of the standard workpiece containing the gray value, it is considered that the workpiece is morphologically defective;
若判断整个工件表面像素点的灰度值不均衡,则认为工件表面不平滑,若判断部分像素点的灰度值小于标准灰度值,则认为工件存在凹陷情况;If it is judged that the gray value of the pixels on the entire surface of the workpiece is unbalanced, it is considered that the surface of the workpiece is not smooth; if the gray value of some pixels is judged to be less than the standard gray value, it is considered that the workpiece is concave;
若判断部分像素点的灰度值大于标准灰度值,则认为工件存在凸起情况;If it is judged that the gray value of some pixel points is greater than the standard gray value, it is considered that the workpiece is convex;
若判断待检测图像存在多种形态问题,则优先列出其与标准工件相差更大的缺陷。If it is judged that there are various morphological problems in the image to be inspected, the defects that are more different from the standard workpiece will be listed first.
可选的,所述工件检测及缺陷判断方法具体为:Optionally, the workpiece detection and defect judgment methods are specifically:
从数模文件中读取工件个数n、位置[XW YW ZW]及大小s1的形态信息,其中数模设计上相关尺寸信息与实际信息相符,原点设计为数模图像上任意一点,将数模坐标系看作世界坐标系;Read the shape information of the number of workpieces n, position [X W Y W Z W ] and size s 1 from the digital-analog file. The relevant size information in the digital-analog design is consistent with the actual information, and the origin is designed to be any arbitrary value on the digital-analog image. One point, regard the digital-analog coordinate system as the world coordinate system;
分别建立图像上的像素坐标系u-v和数模坐标系x-y,并将两个坐标系转换对齐,获取数模坐标点对应位置在图像上的具体坐标位置点;The pixel coordinate system u-v and the digital-analog coordinate system x-y on the image are respectively established, and the two coordinate systems are converted and aligned to obtain the specific coordinate position of the corresponding position of the digital-analog coordinate point on the image;
将世界坐标系转换为像素坐标系的过程是将世界坐标转换为相机坐标,相机坐标转换为图像坐标,图像坐标转换为像素坐标的过程;是四个坐标系间的相互转换;The process of converting the world coordinate system to the pixel coordinate system is the process of converting world coordinates to camera coordinates, camera coordinates to image coordinates, and image coordinates to pixel coordinates; it is the mutual conversion between the four coordinate systems;
其中世界坐标转换为相机坐标:where world coordinates are converted to camera coordinates:
相机坐标转换为图像坐标:Convert camera coordinates to image coordinates:
图像坐标转换为像素坐标:Convert image coordinates to pixel coordinates:
直接将世界坐标转换为像素坐标的过程为:The process of directly converting world coordinates to pixel coordinates is:
其中世界坐标为[XW YW ZW]、相机坐标为[XC YC ZC]、图像坐标:[x y]、像素坐标:[u v]、相机焦距为f、旋转矩阵为R、平移矩阵为T,相机内参为相机外参为 Wherein the world coordinates are [X W Y W Z W ], the camera coordinates are [X C Y C Z C ], the image coordinates: [xy], the pixel coordinates: [uv], the camera focal length is f, the rotation matrix is R, the translation The matrix is T, and the camera internal parameters are Camera external parameters are
将图像上的工件位置坐标[u v]和大小s2信息,并在图像上进行预选框的标注;Put the workpiece position coordinates [uv] and size s 2 information on the image, and mark the pre-selection box on the image;
针对图像的预选框进行图像边缘检测,获取判断对应工件信息;Perform image edge detection on the pre-selected frame of the image to obtain the corresponding workpiece information;
其中边缘检测利用Canny边缘检测算法进行,包含对图像进行灰度化;用高斯滤波器平滑图像;用一阶偏导的有限差分来计算梯度的幅值和方向;对梯度幅值进行非极大值抑制;用双阈值算法检测和连接边缘;The edge detection is performed by the Canny edge detection algorithm, including graying the image; smoothing the image with a Gaussian filter; calculating the magnitude and direction of the gradient with the finite difference of the first-order partial derivative; non-maximizing the magnitude of the gradient Value suppression; detect and connect edges with a dual threshold algorithm;
其中经过高斯滤波后的灰度值变为:The gray value after Gaussian filtering becomes:
每个像素点的梯度强度及方向为:The gradient strength and direction of each pixel are:
用一个高斯矩阵乘以每一个像素点及其邻域,取其带权重的平均值作为最后的灰度值;Multiply each pixel and its neighborhood by a Gaussian matrix, and take its weighted average as the final gray value;
过滤非最大值,使用一个规则来过滤不是边缘的点,使边缘的宽度为1个像素点,形成边缘线;Filter non-maximum values, use a rule to filter points that are not edges, make the width of the edge 1 pixel, and form an edge line;
得到的边缘情况和数模信息对比,判断是否存在工件和工件大小、形态是否满足预先的设计需求;The obtained edge conditions are compared with the digital and analog information to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements;
根据之前检测到的灰度值,得到图像中的一个上阀值和一个下阀值,大于上阀值的都被检测为边缘,而低于下阀值的都被检测为非边缘;对于中间的像素点,如果与确定为边缘的像素点邻接,则判定为边缘;否则为非边缘;According to the previously detected gray value, an upper threshold and a lower threshold in the image are obtained. Those greater than the upper threshold are detected as edges, and those lower than the lower threshold are detected as non-edges; for the middle If it is adjacent to the pixel point determined as an edge, it is determined to be an edge; otherwise, it is a non-edge;
利用模板匹配方法,将模板图像与待测图像或其中某一区域图像进行比较,判断是否相同或相近的过程;Using the template matching method, the template image is compared with the image to be tested or the image of a certain area, and the process of judging whether it is the same or similar;
其中模板匹配中,模板就是一幅已知的小图像,而模板匹配就是在一幅大图像中搜寻目标,已知该图中有要找的目标,且该目标与模板有相同的尺度,方向和图像元素,通过一定算法在图像中找到目标;In template matching, the template is a known small image, and template matching is to search for a target in a large image. It is known that there is a target to be found in the image, and the target has the same scale and direction as the template. and image elements, find the target in the image through a certain algorithm;
利用归一化相关系数匹配法来进行模板匹配Template matching using normalized correlation coefficient matching method
其中将标准工件或数模图像作为模板图像,与待检测图像进行匹配对比,对于其中由拍摄角度或其他原因造成的误差,通过调节匹配阈值来改善,判断工件颜色是否满足预先的设计需求;The standard workpiece or digital-analog image is used as a template image to match and compare with the image to be detected. For the error caused by the shooting angle or other reasons, it can be improved by adjusting the matching threshold to determine whether the color of the workpiece meets the pre-design requirements;
利用工件的H,S,V值对工件进行检测,获取到目标工件中心坐标位置;Use the H, S, V values of the workpiece to detect the workpiece, and obtain the center coordinate position of the target workpiece;
将边缘检测得到的工件中心坐标和色彩检测得到的中心坐标对比,修正得到实际图像上的工件位置坐标;Compare the workpiece center coordinates obtained by edge detection with the center coordinates obtained by color detection, and correct the workpiece position coordinates on the actual image;
对正常形态的待测物图像和存在缺陷的待测物图像分别进行感兴趣区域提取,得到尺寸相同的正常特征图像和缺陷特征图像;The region of interest is extracted for the normal shape of the test object image and the defective test object image respectively, and the normal feature image and the defect feature image with the same size are obtained;
将得到的工件与模板进行匹配对比,确认工件是否满足数模形态信息,确定是否为模板的待检测工件;Match and compare the obtained workpiece with the template, confirm whether the workpiece satisfies the digital-analog shape information, and determine whether it is the workpiece to be detected by the template;
将得到的工件与模板进行匹配对比,判断是否存在工件和工件大小、形态是否满足预先的设计需求;再利用图像灰度值检测方法,对工件形态是否合格进行检测;Match and compare the obtained workpiece with the template to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements; and then use the image gray value detection method to detect whether the shape of the workpiece is qualified;
根据预设尺寸在灰度图中建立选区,统计选区内所有像素点的灰度值,计算选区的灰度均值和标准偏差值;Create a selection area in the grayscale image according to the preset size, count the grayscale values of all pixels in the selection area, and calculate the grayscale mean and standard deviation of the selection area;
判断工件图像灰度值是否符合标准图像,从而判断工件是否为合格工件,综合模板匹配的结果,将对比得到的结果进行输出。Judging whether the gray value of the workpiece image conforms to the standard image, so as to determine whether the workpiece is a qualified workpiece, synthesizing the results of template matching, and outputting the results obtained by comparison.
可选的,所述缺陷包括有无符合的工件、工件形态是否与实际相符、工件位置与是否实际相符和工件颜色是否与实际相符、大小是否与标准相符是否存在凹凸情况和表面是否光滑。Optionally, the defects include whether there is a conforming workpiece, whether the shape of the workpiece conforms to the actual situation, whether the position of the workpiece conforms to the actual situation, whether the color of the workpiece conforms to the actual situation, whether the size conforms to the standard, whether there is unevenness, and whether the surface is smooth.
本发明的有益效果在于:利用数模上中的理想信息,辅助对实际工件图像进行检测。The beneficial effect of the present invention is that the ideal information in the digital model is used to assist in the detection of the actual workpiece image.
本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述,并且在某种程度上,基于对下文的考察研究对本领域技术人员而言将是显而易见的,或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the following description.
附图说明Description of drawings
为了使本发明的目的、技术方案和优点更加清楚,下面将结合附图对本发明作优选的详细描述,其中:In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be preferably described in detail below with reference to the accompanying drawings, wherein:
图1为基于数模信息的图像检测处理方法流程图;1 is a flowchart of an image detection processing method based on digital-analog information;
图2为基于数模颜色的图像方法流程图;Fig. 2 is the flow chart of the image method based on digital-analog color;
图3为工件合格判断流程图。Figure 3 is a flow chart of workpiece qualification judgment.
具体实施方式Detailed ways
以下通过特定的具体实例说明本发明的实施方式,本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用,本说明书中的各项细节也可以基于不同观点与应用,在没有背离本发明的精神下进行各种修饰或改变。需要说明的是,以下实施例中所提供的图示仅以示意方式说明本发明的基本构想,在不冲突的情况下,以下实施例及实施例中的特征可以相互组合。The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.
其中,附图仅用于示例性说明,表示的仅是示意图,而非实物图,不能理解为对本发明的限制;为了更好地说明本发明的实施例,附图某些部件会有省略、放大或缩小,并不代表实际产品的尺寸;对本领域技术人员来说,附图中某些公知结构及其说明可能省略是可以理解的。Among them, the accompanying drawings are only used for exemplary description, and represent only schematic diagrams, not physical drawings, and should not be construed as limitations of the present invention; in order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings will be omitted, The enlargement or reduction does not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the accompanying drawings may be omitted.
本发明实施例的附图中相同或相似的标号对应相同或相似的部件;在本发明的描述中,需要理解的是,若有术语“上”、“下”、“左”、“右”、“前”、“后”等指示的方位或位置关系为基于附图所示的方位或位置关系,仅是为了便于描述本发明和简化描述,而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作,因此附图中描述位置关系的用语仅用于示例性说明,不能理解为对本发明的限制,对于本领域的普通技术人员而言,可以根据具体情况理解上述术语的具体含义。The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms “upper”, “lower”, “left” and “right” , "front", "rear" and other indicated orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must be It has a specific orientation, is constructed and operated in a specific orientation, so the terms describing the positional relationship in the accompanying drawings are only used for exemplary illustration, and should not be construed as a limitation of the present invention. situation to understand the specific meaning of the above terms.
请参阅图1~图3,目前对物体的图像检测不是依赖模板匹配就是利用深度学习对大量数据集的训练,而依赖模型匹配主要是基于形态的匹配,深度学习的检测主要是利用训练图像的图像特征进行检测,很少有检测方法将设计前工件数模中的坐标信息利用到检测中。数模信息包含了各个工作在背景板上的位置信息和工件形态信息。将这些前提条件信息作为一个输入量和待测图像一起输入,再通过各种图像处理的方法,根据工件边缘信息对待测图像进行检测,从而更快速、更准确得获得检测结果。Please refer to Figure 1 to Figure 3. At present, the image detection of objects either relies on template matching or training of a large number of data sets using deep learning, while the model-dependent matching is mainly based on morphology, and the detection of deep learning is mainly based on training images. Image features are detected, and few detection methods use the coordinate information in the digital model of the workpiece before design into the detection. The digital and analog information includes the position information and workpiece shape information of each work on the background plate. Input these precondition information as an input quantity together with the image to be tested, and then use various image processing methods to detect the image to be tested according to the edge information of the workpiece, so as to obtain the detection result more quickly and accurately.
本发明参考工业元器件SMT中的AOI检测方法,利用光学检测方法,通过光的反射检查各类元器件在对应位置是否存在,并且贴装是否正确,焊接是否良好,是否存在各种不良状态。The invention refers to the AOI detection method in the SMT of industrial components, and uses the optical detection method to check whether various components exist in the corresponding positions through the reflection of light, and whether the mounting is correct, whether the welding is good, and whether there are various bad states.
本发明是利用模型匹配的方法将获取到的图像和数模图像进行匹配,从而完成工件的图像检测。The invention uses the method of model matching to match the acquired image with the digital-analog image, so as to complete the image detection of the workpiece.
本发明包含一个成像系统,成像系统可以由一个或多个摄像机组成,以提供更好的图像的可能性。摄像头也应该能够在软件控制下移动,获得更清晰、更完整的图像。The present invention comprises an imaging system which may consist of one or more cameras to provide the possibility of better images. The camera should also be able to move under software control for a sharper, more complete picture.
该方法包含以下步骤:The method includes the following steps:
利用视觉传感器获取完整的工件图像信息;Use vision sensors to obtain complete workpiece image information;
从数模文件中读取工件个数n、位置[XW YW ZW]及大小s1等形态信息,其中由于数模设计上相关尺寸信息与实际信息相符,原点可能设计为数模图像上任意一点,因此可以将数模坐标系理解为世界坐标系;Read morphological information such as the number of workpieces n, position [X W Y W Z W ] and size s 1 from the digital-analog file. Since the relevant size information in the digital-analog design is consistent with the actual information, the origin may be designed as a digital-analog image. on any point, so the digital-analog coordinate system can be understood as the world coordinate system;
分别建立图像上的像素坐标系u-v和数模(世界)坐标系x-y,并将两个坐标系转换对齐,从而获取数模坐标点对应位置在图像上的具体坐标位置点;Establish the pixel coordinate system u-v and the digital-analog (world) coordinate system x-y on the image respectively, and convert and align the two coordinate systems to obtain the specific coordinate position of the corresponding position of the digital-analog coordinate point on the image;
将世界坐标系转换为像素坐标系的过程是将世界坐标转换为相机坐标,相机坐标转换为图像坐标,图像坐标转换为像素坐标的过程。是四个坐标系间的相互转换。The process of converting the world coordinate system to the pixel coordinate system is the process of converting world coordinates to camera coordinates, camera coordinates to image coordinates, and image coordinates to pixel coordinates. It is the mutual conversion between the four coordinate systems.
其中世界坐标转换为相机坐标where world coordinates are converted to camera coordinates
相机坐标转换为图像坐标Convert camera coordinates to image coordinates
图像坐标转换为像素坐标Convert image coordinates to pixel coordinates
直接将世界坐标转换为像素坐标的过程为The process of directly converting world coordinates to pixel coordinates is
其中世界坐标为[XW YW ZW]、相机坐标为[XC YC ZC]、图像坐标:[x y]、像素坐标:[u v]、相机焦距为f、旋转矩阵为R、平移矩阵为T,相机内参为相机外参为 Wherein the world coordinates are [X W Y W Z W ], the camera coordinates are [X C Y C Z C ], the image coordinates: [xy], the pixel coordinates: [uv], the camera focal length is f, the rotation matrix is R, the translation The matrix is T, and the camera internal parameters are Camera external parameters are
将图像上的工件位置坐标[u v]、大小s2等信息,并在图像上进行预选框的标注;The workpiece position coordinates [uv], size s 2 and other information on the image, and mark the pre-selection box on the image;
针对图像的预选框进行图像边缘检测,获取判断对应工件信息;Perform image edge detection on the pre-selected frame of the image to obtain the corresponding workpiece information;
其中边缘检测利用Canny边缘检测算法多个步骤构成,包含对图像进行灰度化;用高斯滤波器平滑图像;用一阶偏导的有限差分来计算梯度的幅值和方向;对梯度幅值进行非极大值抑制;用双阈值算法检测和连接边缘。The edge detection is composed of multiple steps of the Canny edge detection algorithm, including graying the image; smoothing the image with a Gaussian filter; using the finite difference of the first-order partial derivative to calculate the magnitude and direction of the gradient; Non-maximum suppression; detects and connects edges with a two-threshold algorithm.
其中经过高斯滤波后的灰度值将变为:The gray value after Gaussian filtering will become:
每个像素点的梯度强度及方向为The gradient strength and direction of each pixel point are
可以理解为用一个高斯矩阵乘以每一个像素点及其邻域,取其带权重的平均值作为最后的灰度值。It can be understood as multiplying each pixel and its neighborhood by a Gaussian matrix, and taking its weighted average as the final gray value.
过滤非最大值,使用一个规则来过滤不是边缘的点,使边缘的宽度尽可能为1个像素点,形成边缘线。Filter non-maximum values, use a rule to filter points that are not edges, make the width of the edge as 1 pixel as possible, and form an edge line.
得到的边缘情况和数模信息对比,判断是否存在工件和工件大小、形态是否满足预先的设计需求。The obtained edge cases are compared with the digital and analog information to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements.
根据之前检测到的灰度值,得到图像中的一个上阀值和一个下阀值,大于上阀值的都被检测为边缘,而低于下阀值的都被检测为非边缘。对于中间的像素点,如果与确定为边缘的像素点邻接,则判定为边缘;否则为非边缘。这样就可能提高准确度。According to the previously detected gray value, an upper threshold value and a lower threshold value in the image are obtained. Anything greater than the upper threshold value is detected as an edge, and anything lower than the lower threshold value is detected as a non-edge. For a pixel in the middle, if it is adjacent to a pixel determined to be an edge, it is determined to be an edge; otherwise, it is a non-edge. This makes it possible to improve accuracy.
利用模板匹配方法,将模板图像与待测图像或其中某一区域图像进行比较,判断它们是否相同或相近的过程。Using the template matching method, the template image is compared with the image to be tested or the image of a certain area, and it is a process of judging whether they are the same or similar.
其中模板匹配中,模板就是一幅已知的小图像,而模板匹配就是在一幅大图像中搜寻目标,已知该图中有要找的目标,且该目标与模板有相同的尺度,方向和图像元素,通过一定的算法可以在图像中找到目标。In template matching, the template is a known small image, and template matching is to search for a target in a large image. It is known that there is a target to be found in the image, and the target has the same scale and direction as the template. and image elements, the target can be found in the image through a certain algorithm.
利用归一化相关系数匹配法来进行模板匹配Template matching using normalized correlation coefficient matching method
其中将标准工件或数模图像作为模板图像,与待检测图像进行匹配对比,对于其中由拍摄角度或其他原因造成的误差,可以通过调节匹配阈值来改善,判断工件颜色是否满足预先的设计需求。The standard workpiece or digital-analog image is used as a template image to match and compare with the image to be detected. For the error caused by the shooting angle or other reasons, it can be improved by adjusting the matching threshold to determine whether the color of the workpiece meets the pre-design requirements.
利用工件的H,S,V值对工件进行检测,获取到目标工件中心坐标位置。Use the H, S, V values of the workpiece to detect the workpiece, and obtain the center coordinate position of the target workpiece.
将边缘检测得到的工件中心坐标和色彩检测得到的中心坐标对比,修正得到实际图像上的工件位置坐标。Compare the workpiece center coordinates obtained by edge detection with the center coordinates obtained by color detection, and correct the workpiece position coordinates on the actual image.
对正常形态的待测物图像和存在缺陷的待测物图像分别进行感兴趣区域提取,得到尺寸相同的正常特征图像和缺陷特征图像。The region of interest is extracted for the normal shape of the test object image and the defective test object image, respectively, to obtain the normal feature image and the defect feature image with the same size.
将得到的工件与模板进行匹配对比,确认工件是否满足数模形态信息,确定是否为模板的待检测工件;Match and compare the obtained workpiece with the template, confirm whether the workpiece satisfies the digital-analog shape information, and determine whether it is the workpiece to be detected by the template;
将得到的工件与模板进行匹配对比,判断是否存在工件和工件大小、形态是否满足预先的设计需求;再利用图像灰度值检测方法,对工件形态是否合格进行检测。Match and compare the obtained workpiece with the template to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements; and then use the image gray value detection method to detect whether the shape of the workpiece is qualified.
根据预设尺寸在灰度图中建立选区,统计选区内所有像素点的灰度值,计算选区的灰度均值和标准偏差值;Create a selection area in the grayscale image according to the preset size, count the grayscale values of all pixels in the selection area, and calculate the grayscale mean and standard deviation of the selection area;
判断工件图像灰度值是否符合标准图像,从而判断工件是否为合格工件,综合模板匹配的结果,将对比得到的结果进行输出。Judging whether the gray value of the workpiece image conforms to the standard image, so as to determine whether the workpiece is a qualified workpiece, synthesizing the results of template matching, and outputting the results obtained by comparison.
其中若部分判断包含灰度值的像素点范围超出标准工件包含灰度值的像素点范围,则认为工件形态过大,若部分判断包含灰度值的像素点少于标准工件包含灰度值的像素点范围,则认为工件形态缺损,若判断整个工件表面像素点的灰度值不均衡,则认为工件表面不平滑,若判断部分像素点的灰度值小于标准灰度值,则认为工件存在凹陷情况,若判断部分像素点的灰度值大于标准灰度值,则认为工件存在凸起情况。Among them, if the range of pixels that are partially judged to contain gray values exceeds the range of pixels that contain gray values of the standard workpiece, it is considered that the shape of the workpiece is too large. If it is judged that the gray value of the pixels on the entire surface of the workpiece is unbalanced, it is considered that the surface of the workpiece is not smooth. If the gray value of some pixels is judged to be less than the standard gray value, it is considered that the workpiece exists. In the case of concave, if it is judged that the gray value of some pixel points is greater than the standard gray value, it is considered that the workpiece is convex.
若判断待检测图像存在多种形态问题,则优先列出其与标准工件相差更大的缺陷。If it is judged that there are various morphological problems in the image to be inspected, the defects that are more different from the standard workpiece will be listed first.
该检测方法可以对具有对应数模信息且颜对比色明显的工件进行检测,并确定与理想数模信息存在的偏差,对工件检测相应的缺陷检测,并且能够在不中断生产流程的情况下进行检测。在生产中得到工件的检测信息,能够实现更好的过程控制,早期发现缺陷将避免将后期工件的装配和分装,能够提前对工件进行维修。The detection method can detect workpieces with corresponding digital-analog information and obvious color contrast, and determine the deviation from the ideal digital-analog information, detect corresponding defects on the workpiece, and can detect without interrupting the production process. . Obtaining the inspection information of the workpiece in production can achieve better process control. Early detection of defects will avoid the assembly and subassembly of the workpiece in the later stage, and the workpiece can be repaired in advance.
这里的缺陷主要包括有无符合的工件,工件形态是否与实际相符,工件位置与是否实际相符和工件颜色是否与实际相符,大小是否与标准相符,是否存在凹凸情况,表面是否光滑等。The defects here mainly include whether there is a conforming workpiece, whether the shape of the workpiece is consistent with the actual, whether the position of the workpiece is consistent with the actual, whether the color of the workpiece is consistent with the actual, whether the size is consistent with the standard, whether there is unevenness, whether the surface is smooth, etc.
最后说明的是,以上实施例仅用以说明本发明的技术方案而非限制,尽管参照较佳实施例对本发明进行了详细说明,本领域的普通技术人员应当理解,可以对本发明的技术方案进行修改或者等同替换,而不脱离本技术方案的宗旨和范围,其均应涵盖在本发明的权利要求范围当中。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should all be included in the scope of the claims of the present invention.
Claims (3)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210593470.8A CN114897864B (en) | 2022-05-27 | 2022-05-27 | Workpiece detection and defect judgment method based on digital model information |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210593470.8A CN114897864B (en) | 2022-05-27 | 2022-05-27 | Workpiece detection and defect judgment method based on digital model information |
Publications (2)
Publication Number | Publication Date |
---|---|
CN114897864A true CN114897864A (en) | 2022-08-12 |
CN114897864B CN114897864B (en) | 2024-04-12 |
Family
ID=82726842
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210593470.8A Active CN114897864B (en) | 2022-05-27 | 2022-05-27 | Workpiece detection and defect judgment method based on digital model information |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114897864B (en) |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115257767A (en) * | 2022-09-26 | 2022-11-01 | 中科慧眼(天津)研究开发有限公司 | Pavement obstacle height measurement method and system based on plane target |
CN115731165A (en) * | 2022-09-28 | 2023-03-03 | 广州市易鸿智能装备有限公司 | Detection system and method for lithium battery online size point inspection |
CN116580022A (en) * | 2023-07-07 | 2023-08-11 | 杭州鄂达精密机电科技有限公司 | Workpiece size detection method, device, computer equipment and storage medium |
CN116703914A (en) * | 2023-08-07 | 2023-09-05 | 浪潮云洲工业互联网有限公司 | Welding defect detection method, equipment and medium based on generation type artificial intelligence |
CN116818780A (en) * | 2023-05-26 | 2023-09-29 | 深圳市大德激光技术有限公司 | Visual 2D and 3D detection system for button cell shell after laser welding |
CN117152145A (en) * | 2023-10-31 | 2023-12-01 | 威海天拓合创电子工程有限公司 | Board card process detection method and device based on image |
CN117974605A (en) * | 2024-02-02 | 2024-05-03 | 山东福茂装饰材料有限公司 | Method for detecting edge sealing defect of plate based on image |
CN118010751A (en) * | 2024-04-08 | 2024-05-10 | 杭州汇萃智能科技有限公司 | A machine vision detection method and system for workpiece defect detection |
CN118396942A (en) * | 2024-04-22 | 2024-07-26 | 泰安鑫杰机械有限公司 | Intelligent sensing and fault diagnosis method for transformer winding insulating part processing equipment |
CN118657734A (en) * | 2024-06-20 | 2024-09-17 | 深圳市美矽微视觉技术有限公司 | Chip surface defect detection method, device and storage medium |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090161943A1 (en) * | 2007-12-25 | 2009-06-25 | Hitachi High-Technologies Corporation | Inspection apparatus and inspection method |
EP2998927A1 (en) * | 2014-09-22 | 2016-03-23 | ALSTOM Transport Technologies | Method for detecting the bad positioning and the surface defects of specific components and associated detection device |
US20210010953A1 (en) * | 2019-07-12 | 2021-01-14 | SVXR, Inc. | Methods and Systems for Defects Detection and Classification Using X-rays |
CN113016004A (en) * | 2018-11-16 | 2021-06-22 | 阿莱恩技术有限公司 | Machine-based three-dimensional (3D) object defect detection |
CN113034474A (en) * | 2021-03-30 | 2021-06-25 | 无锡美科微电子技术有限公司 | Test method for wafer map of OLED display |
CN114240854A (en) * | 2021-11-30 | 2022-03-25 | 深圳市裕展精密科技有限公司 | Product detection method and detection device |
WO2022065621A1 (en) * | 2020-09-28 | 2022-03-31 | (주)미래융합정보기술 | Vision inspection system using distance learning of product defect image |
-
2022
- 2022-05-27 CN CN202210593470.8A patent/CN114897864B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090161943A1 (en) * | 2007-12-25 | 2009-06-25 | Hitachi High-Technologies Corporation | Inspection apparatus and inspection method |
EP2998927A1 (en) * | 2014-09-22 | 2016-03-23 | ALSTOM Transport Technologies | Method for detecting the bad positioning and the surface defects of specific components and associated detection device |
CN113016004A (en) * | 2018-11-16 | 2021-06-22 | 阿莱恩技术有限公司 | Machine-based three-dimensional (3D) object defect detection |
US20210010953A1 (en) * | 2019-07-12 | 2021-01-14 | SVXR, Inc. | Methods and Systems for Defects Detection and Classification Using X-rays |
WO2022065621A1 (en) * | 2020-09-28 | 2022-03-31 | (주)미래융합정보기술 | Vision inspection system using distance learning of product defect image |
CN113034474A (en) * | 2021-03-30 | 2021-06-25 | 无锡美科微电子技术有限公司 | Test method for wafer map of OLED display |
CN114240854A (en) * | 2021-11-30 | 2022-03-25 | 深圳市裕展精密科技有限公司 | Product detection method and detection device |
Non-Patent Citations (2)
Title |
---|
JHON GOMEZ: "DDtM: Increasing Latent Defect Detection in Analog/Mixed-Signal ICs Using the Difference in Distance to Mean Value", 《IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS》, 29 September 2021 (2021-09-29), pages 4771 * |
龙亮亮;刘冠峰;张国英;李永胜;: "基于机器视觉的玻璃马赛克缺陷在线检测系统", 机械设计与制造, no. 1, 8 September 2017 (2017-09-08), pages 136 - 139 * |
Cited By (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115257767B (en) * | 2022-09-26 | 2023-02-17 | 中科慧眼(天津)研究开发有限公司 | Road surface obstacle height measurement method and system based on plane target |
CN115257767A (en) * | 2022-09-26 | 2022-11-01 | 中科慧眼(天津)研究开发有限公司 | Pavement obstacle height measurement method and system based on plane target |
CN115731165A (en) * | 2022-09-28 | 2023-03-03 | 广州市易鸿智能装备有限公司 | Detection system and method for lithium battery online size point inspection |
CN115731165B (en) * | 2022-09-28 | 2023-10-20 | 广州市易鸿智能装备有限公司 | Detection system and method for on-line size spot inspection of lithium battery |
CN116818780B (en) * | 2023-05-26 | 2024-03-26 | 深圳市大德激光技术有限公司 | Visual 2D and 3D detection system for button cell shell after laser welding |
CN116818780A (en) * | 2023-05-26 | 2023-09-29 | 深圳市大德激光技术有限公司 | Visual 2D and 3D detection system for button cell shell after laser welding |
CN116580022A (en) * | 2023-07-07 | 2023-08-11 | 杭州鄂达精密机电科技有限公司 | Workpiece size detection method, device, computer equipment and storage medium |
CN116580022B (en) * | 2023-07-07 | 2023-09-29 | 杭州鄂达精密机电科技有限公司 | Workpiece size detection method, device, computer equipment and storage medium |
CN116703914A (en) * | 2023-08-07 | 2023-09-05 | 浪潮云洲工业互联网有限公司 | Welding defect detection method, equipment and medium based on generation type artificial intelligence |
CN116703914B (en) * | 2023-08-07 | 2023-12-22 | 浪潮云洲工业互联网有限公司 | Welding defect detection method, equipment and medium based on generation type artificial intelligence |
CN117152145A (en) * | 2023-10-31 | 2023-12-01 | 威海天拓合创电子工程有限公司 | Board card process detection method and device based on image |
CN117152145B (en) * | 2023-10-31 | 2024-02-23 | 威海天拓合创电子工程有限公司 | Board card process detection method and device based on image |
CN117974605A (en) * | 2024-02-02 | 2024-05-03 | 山东福茂装饰材料有限公司 | Method for detecting edge sealing defect of plate based on image |
CN118010751A (en) * | 2024-04-08 | 2024-05-10 | 杭州汇萃智能科技有限公司 | A machine vision detection method and system for workpiece defect detection |
CN118396942A (en) * | 2024-04-22 | 2024-07-26 | 泰安鑫杰机械有限公司 | Intelligent sensing and fault diagnosis method for transformer winding insulating part processing equipment |
CN118657734A (en) * | 2024-06-20 | 2024-09-17 | 深圳市美矽微视觉技术有限公司 | Chip surface defect detection method, device and storage medium |
Also Published As
Publication number | Publication date |
---|---|
CN114897864B (en) | 2024-04-12 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN114897864B (en) | Workpiece detection and defect judgment method based on digital model information | |
CN108776140B (en) | Machine vision-based printed matter flaw detection method and system | |
CN101673397B (en) | Digital camera nonlinear calibration method based on LCDs | |
CN103345755B (en) | A kind of Chessboard angular point sub-pixel extraction based on Harris operator | |
CN111260731A (en) | Checkerboard sub-pixel level corner point self-adaptive detection method | |
CN113570605B (en) | Defect detection method and system based on liquid crystal display panel | |
CN107705293A (en) | A kind of hardware dimension measurement method based on CCD area array cameras vision-based detections | |
CN115082394A (en) | Plug-in defect visual detection and identification method, readable storage medium and device | |
CN108007388A (en) | A kind of turntable angle high precision online measuring method based on machine vision | |
CN112686920A (en) | Visual measurement method and system for geometric dimension parameters of circular part | |
CN106226325A (en) | A kind of seat surface defect detecting system based on machine vision and method thereof | |
CN109815822B (en) | Patrol diagram part target identification method based on generalized Hough transformation | |
CN111127417B (en) | Printing defect detection method based on SIFT feature matching and SSD algorithm improvement | |
CN117058411B (en) | Method, device, medium and equipment for identifying edge appearance flaws of battery | |
CN114677356A (en) | Wine bottle appearance defect detection method based on multi-view image fusion | |
CN112200790B (en) | Cloth defect detection method, device and medium | |
CN114998571B (en) | Image processing and color detection method based on fixed-size markers | |
CN108876842A (en) | A kind of measurement method, system, equipment and the storage medium of sub-pixel edge angle | |
CN114963981B (en) | A non-contact measurement method for cylindrical parts docking based on monocular vision | |
CN114219802B (en) | Skin connecting hole position detection method based on image processing | |
CN116051808A (en) | YOLOv 5-based lightweight part identification and positioning method | |
CN112381751A (en) | Online intelligent detection system and method based on image processing algorithm | |
JP4814116B2 (en) | Mounting board appearance inspection method | |
JP2002140713A (en) | Image processing method and image processor | |
CN209279912U (en) | A kind of object dimensional information collecting 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 |