Defect detection algorithm of strip surface based on STCS-YOLO
ZHOU Yaluo1, WU Xianchao1, LIU Wenguang2, ZHANG Ruicheng1
1. Collage of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China; 2. Shougang Jingtang Limited Iron and Steel Co., Ltd., Tangshan 063200, Hebei, China
Abstract：Strip steel surface defect detection technology is one of the important technologies for the production of high quality strip steel products. Aiming at the problems of misdetection, inaccurate positioning, and poor detection ability of small-scale defect targets in previous strip surface defect detection, a strip surface defect detection algorithm (STCS-YOLO) of improved YOLOv5 was proposed. Firstly, the Swin Transformer module was used to fuse with the original C3 module in the output part of feature fusion network to enhance the interaction and reuse of global feature information, which significantly improved the detection ability of small-scale defect targets. Secondly, a lightweight upsampling operator CARAFE was used to replace the traditional sampling operation to better recover defect information, which improved the recognition accuracy of strip surface defects. Finally, embedding 3-D weight attention mechanism SimAM in feature extraction network to enhance the ability of focusing on the foreground feature information, which improved the strong identification ability of defect target. The experimental results demonstrate that the proposed algorithm achieves 79.7% of PmA(mean average precision) on the NEU-DET dataset, which is improvement of 3.9 percentage points over the original network. Additionally, while maintaining model weight and computational complexity nearly unchanged, the single-frame detection time reaches 10.9 ms, which can basically meet the requirements of accurate and rapid detection of strip surface defects. The strip surface defect detection algorithm proposed in this paper lays the technological foundation for improving neat and flawless high quality strip products.
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2023, Vol. 33 
