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This document presents an improved algorithm for real-time defect detection in steel, named YOLOv5s-7.0-FCC, which enhances the accuracy and speed of defect identification by integrating advanced modules into the YOLOv5 architecture. The proposed algorithm demonstrates a mean average precision (mAP) increase from 73.1% to 79.5% and an improved detection speed from 101.1 f/s to 109.4 f/s, addressing the limitations of existing object detection algorithms in steel manufacturing. The study emphasizes the impor

Author

sanae

Language

EN