A method for detecting an automobile seat skeleton stamping part based on machine vision positioning

By constructing a deformation vector field and a texture entropy field, adaptively adjusting the window size, and integrating streamline vectors and micro-texture directions, the problem of distinguishing between deformation texture and defect texture in the inspection of stamped automotive seat frame parts is solved, achieving high-precision and high-robust defect detection.

CN122391224APending Publication Date: 2026-07-14WUHAN MINGYU METAL PARTS CO LTD
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WUHAN MINGYU METAL PARTS CO LTD
Filing Date
2026-06-12
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing methods for inspecting stamped automotive seat frames suffer from insufficient accuracy and robustness when dealing with deformation and texture interference, making it difficult to effectively distinguish between deformation textures and defect textures.

Method used

By constructing a deformation vector field and a texture entropy field, adaptively adjusting the window size, and combining streamline vectors and micro-texture directions, a crack significance index is constructed to achieve accurate positioning of stamping defects.

Benefits of technology

It significantly improves the accuracy and robustness of stamping defect detection, effectively distinguishes between normal and abnormal textures, and enhances the accuracy and stability of detection.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122391224A_ABST
    Figure CN122391224A_ABST
Patent Text Reader

Abstract

The present application relates to the technical field of automobile manufacturing, in particular to a kind of automobile seat framework stamping part detection method based on machine vision positioning. Its method includes: first, the surface gray image of stamping part is collected, the edge profile is extracted and matched with standard template to obtain the region to be measured;Then based on the displacement vector of edge profile, construct deformation vector field, based on local texture feature distribution, construct texture entropy field;Then according to the length of deformation vector field, the window size is adaptively adjusted and the microtexture direction is extracted;Finally, the crack significant index is constructed and the defect is positioned by integrating streamline vector, microtexture direction and texture entropy field. The present application improves the texture extraction accuracy by adaptive window adjustment guided by deformation vector field, and significantly improves the accuracy and robustness of crack detection by comprehensive evaluation of direction difference and texture quality.
Need to check novelty before this filing date? Find Prior Art