A cross-scale industrial surface defect detection method and system
By combining Blob analysis and Homography estimation neural networks, a cross-scale industrial surface defect detection method has been developed, which solves the problems of high computational cost and weak robustness in existing technologies and achieves efficient identification and detection of small-scale defects.
CN116563325BActive Publication Date: 2026-06-19GUANGDONG POLYTECHNIC NORMAL UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGDONG POLYTECHNIC NORMAL UNIV
- Filing Date
- 2023-04-03
- Publication Date
- 2026-06-19
AI Technical Summary
Technical Problem
Existing industrial surface defect detection methods are computationally intensive, have weak robustness, and low detection efficiency, especially in effectively identifying small-sized defects among multi-scale defects.
Method used
By employing the Blob analysis algorithm combined with the Homography estimation neural network for image feature extraction and alignment calibration, and combining it with a sliding window for noise filtering, cross-scale industrial surface defect detection is achieved.
Benefits of technology
It improves the ability to identify small-scale defects, enhances the robustness and generalization ability of the method, and improves detection efficiency.
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Smart Images
Figure CN116563325B_ABST
Abstract
This invention relates to the field of industrial defect detection technology, and proposes a cross-scale industrial surface defect detection method and system, including the following steps: acquiring template image I a And the industrial product image to be inspected I b And preprocess it; based on the Blob algorithm, the preprocessed image I a I b Feature extraction is performed to obtain image features I'. a 、I' b The image I is estimated using a Homography estimation neural network. a and I b and image features I' a and I' b Perform alignment calibration separately; for the aligned and calibrated image I a I b The difference image is processed by subtraction, and a sliding window is used to filter noise from the difference image to obtain the industrial surface defect detection result. This invention introduces a Blob analysis algorithm to locate small-scale defects in multi-scale defects, and combines it with a Homography estimation neural network to align and calibrate the standard sample image used for the defect sample to be detected, so as to achieve better difference results.
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