查看文章

Strip steel is an indispensable material in the manufacturing industry and the defects of the surface directly determine the quality. Due to the diversity and complexity of surface defects in intraclass and between interclass, a great deal of manpower and resources have been devoted to surface defect detection. This article proposes a new deep learning detection network, channel attention, and bidirectional feature fusion on a fully convolutional one-stage (CABF-FCOS) network to achieve rapid and effective defect detection on steel strips. First, the anchor-free FCOS is proposed as the detection framework to eliminate affections of those hyperparameters related to the anchor. Second, a channel attention mechanism (CAM) module is proposed to reduce the loss of feature information. Finally, it replaces the feature pyramid network (FPN) with a bidirectional feature fusion network (BFFN) for more effective feature fusion …