The wire rope used in coal mine plays an important application value in mine operation,and its reliability is directly related to the operation efficiency of the mine and the life safety of the staff. Aiming at the problems of low detection accuracy and insufficient detection efficiency of existing wire rope surface defects. This paper proposes an improved YOLOv8 detection algorithm YOLO_BF. Firstly,an improved double-layer link attention mechanism (BiFormer) is introduced into the backbone network to enhance the model 's ability to analyze images and information fusion,which significantly improves the accuracy of the model. Secondly,the repeated weighted bidirectional feature pyramid network (BiFPN) is embedded to improve the ability of network defect feature extraction. On this basis,WIoU is used to improve the convergence speed of the model. Finally,GhostConv is used to replace the traditional convolution to realize the lightweight of the model. Compared with the original basic network YOLOv8n,the accuracy,recall and average accuracy are increased by 2.3％,3.3％ and 5.2％ respectively.It is more in line with the practical application requirements of wire rope damage detection.
关键词
钢丝绳/缺陷检测/YOLO/注意力机制/损失函数
Key words
wire rope/defect detection/YOLO/attention mechanism/loss function
维普
万方数据