Workpiece defect detection system and method based on image recognition

By mapping image grayscale data to a gradient potential field and introducing virtual hydrodynamic evolution, combined with feedback optimization and few-sample calibration, the problem of low signal-to-noise ratio in the detection of weak defects on complex textured surfaces is solved, achieving efficient and flexible defect detection.

CN121707983APending Publication Date: 2026-03-20DONGYING JIASHENG NEW MATERIALS CO LTD
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Patent Information

Application Number
CN202511923307.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-20

AI Technical Summary

Technical Problem

Existing technologies have low signal-to-noise ratios in detecting weak defects on complex textured surfaces, making it difficult to effectively separate background textures from weak defects. Furthermore, deep learning methods rely on training with a large number of samples, which is difficult to obtain in precision manufacturing scenarios.

Method used

By mapping image grayscale data to a gradient potential energy field, a virtual fluid is introduced for dynamic evolution. Defects are identified by calculating the divergence of the flow field disturbance, and the system parameters are dynamically adjusted by combining a feedback optimization mechanism. A few-sample calibration method and hardware acceleration technology are employed.

Benefits of technology

It significantly improves the detection capability of weak defects in complex backgrounds, reduces the false negative rate, adapts to the consistency of inspection standards for different batches of workpieces, and takes into account the needs of rapid deployment and real-time detection.

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Abstract

The invention relates to the technical field of machine vision and industrial automatic detection, in particular to a workpiece defect detection system and method based on image recognition. An image mapping module, a flow field initialization module, a dynamic evolution module, a divergence identification module and a feedback optimization module are included; the system maps the gray scale into a terrain gradient potential energy field, and establishes a virtual flow field conforming to the texture; the core of the method is that dynamic evolution is carried out based on a Navier-Stokes equation, defects are converted into physical obstacles inducing flow field disturbance, and a defect area is judged according to divergence values; dynamically adjusting an evolution parameter according to the verification signal; according to the method, conversion from pixel statistics to physical simulation is realized, and the problem that weak defects are difficult to extract under a complex background is solved.
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