Detection of Surface Defects in Hot-rolled Strip Steel Based on Lightweight YOLO Network
Aiming at the problems of low detection accuracy and scale sensitivity of convolutional features in hot-rolled strip surface defect detection,this paper designs an efficient feature extraction module(FEM)and an enhanced multi-scale feature module(MFM),and proposes a deep learning-based lightweight hot-rolled strip surface defect detection method,Better Lightweight YOLO(BL-YOLO).Experimental results show that this defect detection network achieves a good balance between performance and consumption,achieving a mAP of 80.1 with 61.9 fps.
deep learningdefect detectionNEU-DETlightweighting techniquemulti-scale strategy
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