The detection of small target defects on the surface of hot rolled strip steel is a hot research topic in the field of industrial quality inspection.An improved YOLOv7 algorithm is proposed for the problem of small target defects prone to miss detection in hot rolled strip steel surface defect inspec-tion tasks.Convolutional block attention module(CBAM)module and RepConv module are incorpo-rated in the backbone network to improve the efficiency of small target feature extraction.The origi-nal path aggregation network(PANet)neck network is replaced by the improved bi-directional feature pyramid network(BiFPN)neck network to achieve efficient purification of small target defect fea-tures.Decoupled detection heads are used for detection result output,so that the network can further converge to higher accuracy during training.Finally,it is shown experimentally that the improved YOLOv7 is ahead of YOLOv7 algorithm by 4.3 AP50 accuracy,ahead of YOLOv6 algorithm by 5.0 AP50 accuracy and ahead of YOLOX algorithm by 4.8 AP50 accuracy in detecting defects in small tar-get hot rolled strip steel detection scenarios.The proposed algorithm can be better applied to small target strip steel defect detection.
维普
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