A lightweight steel surface defect detection method, device and processing equipment

By improving the C3k2MFB structure of the YOLOv11 model and introducing multiple modules, the problems of low efficiency and insufficient accuracy in traditional detection methods have been solved, achieving efficient and lightweight detection of steel surface defects.

CN122368626APending Publication Date: 2026-07-10WENHUA UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WENHUA UNIV
Filing Date
2026-04-23
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Traditional steel surface defect detection relies on manual operation, which is inefficient and costly. Furthermore, existing computer vision methods struggle to achieve both high detection accuracy and lightweight design, leading to missed detections and false detections.

Method used

Based on the YOLOv11 model, the backbone and neck network are reconstructed through the C3k2MFB structure. The MultiScaleFeatureMixer and DynamicSepConv2d modules are combined to enhance the feature representation capability. The adaptability and detection accuracy of the model are improved through linear deformable convolution and Triplet Attention modules.

Benefits of technology

While ensuring high detection accuracy, it effectively reduces the number of parameters and computational load, has good generalization ability, and is suitable for lightweight detection in industrial terminals.

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Abstract

The application provides a lightweight steel surface defect detection method and device and processing equipment, which are based on a YOLOv11 model, deep adaptability optimization is performed, the steel surface defect detection task has better detection precision, the parameter quantity and the calculation quantity can be effectively reduced, the generalization ability is good, when the industrial terminal is deployed, the high performance and the lightweight can be considered, and thus the industrial application prospect is good.
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