Product quality control method and system based on machine vision

By calculating the grayscale gradient and multi-scale texture changes on the product surface and adjusting the position of boundary points, accurate classification of complex defects is achieved, solving the problem of limited detection sensitivity in existing technologies and improving the accuracy and stability of detection.

CN120997124AInactive Publication Date: 2025-11-21长春科技学院
0 Cites 9 Cited by

Patent Information

Application Number
CN202510936885.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-08
Publication Date
2025-11-21
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing machine vision-based product quality inspection methods are ill-suited to complex textures and subtle structural variations. Defect region screening is based on single-scale analysis, which limits detection sensitivity, leads to boundary positioning bias, and results in insufficient classification accuracy.

Method used

By calculating the grayscale gradient value of the product surface, extracting the grayscale gradient change rate, generating a multi-scale texture mutation point set, calculating the directional consistency mutation region, adjusting the boundary point position, and classifying defect types by combining gradient distribution patterns.

Benefits of technology

It improves the accuracy of identifying defective areas in products, enhances the ability to distinguish complex defects, reduces false detections and missed detections, and ensures the stability and adaptability of automated quality inspection.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of quality detection, in particular to a product quality control method and system based on machine vision, and the method comprises the following steps: obtaining product surface image data, calculating the gray gradient value of each pixel, extracting the gray gradient change rate, recording the gradient amplitude and direction information, and generating product surface gradient data. According to the method, through pixel-level gray scale gradient calculation, the product local feature expression ability is improved, multi-scale gradient change trend analysis is combined, the accurate recognition ability of a product defect area is improved, through texture direction angle calculation and vector field construction, the direction change anomaly detection reliability is enhanced, and the direction change anomaly detection accuracy is improved based on the combination of a direction deviation accumulated value and an abrupt change threshold value. Effective identification of a structure sudden change area is ensured, adjustment is carried out for curvature continuity abnormal points, defect boundary fitting precision is optimized, defect area internal gradient distribution and boundary feature comparative analysis are carried out, accurate classification of defect types is realized, and stability and adaptability of automatic product quality detection are ensured.
Need to check novelty before this filing date? Find Prior Art