Steel coil end face defect detection method and system, training method and electronic equipment

By simultaneously acquiring and fusing RGB-D multimodal detection methods with two-dimensional images and three-dimensional point cloud data, the accuracy and robustness issues of steel coil end-face defect detection have been solved, enabling efficient identification and automated quality inspection of complex and subtle defects.

CN121883480APending Publication Date: 2026-04-17上海研视信息科技有限公司
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Patent Information

Application Number
CN202610320151.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-17
Publication Date
2026-04-17

AI Technical Summary

Technical Problem

Existing steel coil end-face defect detection technologies suffer from low detection accuracy, weak robustness, and low degree of automation integration, making it particularly difficult to identify complex three-dimensional defects and micro-cracks.

Method used

By controlling the motion actuator to synchronously acquire two-dimensional image data and three-dimensional point cloud data, spatiotemporal registration is performed to generate RGB-D multimodal data. Surface texture and geometric structure features are extracted using a multimodal fusion neural network, and weighted fusion is performed through a cross-modal attention mechanism. Finally, the geometric feature consistency is verified by combining the three-dimensional point cloud data, and the defect detection results are output.

Benefits of technology

It significantly improves the accuracy and robustness of identifying complex and subtle defects, reduces the false detection rate, and achieves efficient automated steel coil end face quality inspection. It adapts to complex industrial site conditions and supports automatic marking and production line integration.

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Abstract

The invention discloses a steel coil end face defect detection method and system, a training method and electronic equipment, and the steel coil end face defect detection method comprises the steps: driving a two-dimensional image and three-dimensional point cloud collection equipment to synchronously scan the end face of a steel coil through a movement mechanism, and obtaining registered RGB-D multi-modal data; inputting the data into a special detection model, extracting surface texture and geometric structure features, performing dynamic weighted fusion through channel splicing and a cross-modal attention mechanism, and outputting a suspected defect result containing defect types, positions, three-dimensional depth information and confidence; and carrying out geometric feature consistency verification on the suspected defect by combining the original point cloud, and finally determining a real defect. According to the model, deep synergy of texture and geometric features is innovatively realized, the recognition accuracy and robustness of complex defects such as micro cracks and recesses under reflection interference are remarkably improved, and meanwhile, the detection efficiency is guaranteed.
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