A detection network for small defects of steel surface based on YOLOv7

Abstract

Defect detection is crucial in steel production to prevent safety risks and prolong lifespan. However, current methods still face challenges in accurately detecting small and vague targets. In response to this problem, we propose a model named SRN-YOLO for steel surface detection based on YOLOv7. Firstly, a split residual convolution network (SResNet) is designed to capture gradient feature information. Then, a re-fusion feature pyramid network (RFPN) is built to minimize the loss of features. Furthermore, a location regression loss function named NWD-CIOU combined the Normalized Wasserstein Distance (NWD) and Complete Intersection Over Union (CIOU) is employed to increase the number of positive samples of small targets. The experiment results show that SRN-YOLO has excellent detection performance for steel surface defects. Specifically, the recognition accuracies of the model on NEU-DET and GC10-DET reach 81.2 mAP and 71.6 mAP, respectively, which are improved by 5.8% and 3.8% compared to YOLOv7.