Method for detecting component pins based on machine vision
By acquiring the 3D point cloud of the component and converting it into a height map, setting the selection range, and using the effective point cloud to form a grayscale image, combined with Blob analysis and centroid algorithm, the problem of pin detection distortion caused by noisy images is solved, and more accurate defect judgment is achieved.
CN122109122APending Publication Date: 2026-05-29SHANGHAI SENGO ADVANCED TECHNOLOGY CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI SENGO ADVANCED TECHNOLOGY CO LTD
- Filing Date
- 2026-01-28
- Publication Date
- 2026-05-29
AI Technical Summary
Technical Problem
In existing technologies, when using Blob analysis, pin detection is distorted due to noisy images, leading to incorrect defect identification.
Method used
By acquiring the 3D point cloud of the component being inspected, converting it into a height map, setting the selection range, using the effective point cloud to form a grayscale image, and combining Blob analysis and centroid algorithm to perform pin detection, the influence of noisy point cloud is filtered out.
Benefits of technology
It effectively avoids the adverse effects of noisy point clouds on grayscale images, ensures the accuracy of pin detection, and reduces misjudgments in defect identification.
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Smart Images
Figure CN122109122A_ABST
Abstract
The application discloses a kind of element pin detection methods based on machine vision, comprising the following steps: step one: using industrial camera obtains the 3D point cloud of the detected element including pin;Step two: based on the 3D point cloud obtained in step one, obtain height map;Step three: in height map, select a height reference value, and form a selection interval on the height reference value according to the set height difference value;Step four: the point cloud of the sampling point in the selection interval determined in step three is used to form a gray scale image;Step five: in the gray scale image, the center of gravity of the pin is obtained using Blob analysis algorithm and center of gravity algorithm;Step six: use the center of gravity of the pin obtained in step five to judge defects, so use height map to set selection interval to select effective point cloud, so noise point cloud can be filtered, avoid noise point cloud adversely affect gray scale image, to form the real gray scale image of pin, and then judge the defects of pin.
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