TBi-YOLOv5: A surface defect detection model for crane wire with Bottleneck Transformer and small target detection layer

Abstract

Surface defect detection of crane wire rope is very important for safe operations of crane. However, the surface defect characteristics of crane wire rope are varied, there is no standardized data and tiny-size, which makes the universal surface defect detection models ineffective. Therefore, a surface defect detection model named TBi-YOLOv5 is proposed in this article. First, a random augment method is used to search the optimal data augment strategy automatically and enforce the generalization ability of the surface defect detection model. Then, in order to enhance the feature extraction ability on both the local and global views, a Bottleneck Transformer (BOT) module is added in YOLOv5, which makes effective fusion of convolutional neural network (CNN) and multi-head self-attention mechanism (MHSA). After that, inspired by the idea of bidirectional feature pyramid network (BiFPN), a small target detection layer (STPL) is added in the neck part of YOLOv5 to detect the tiny-size surface defect on the wire rope. Experiments on the surface defect detection of crane wire rope show the effectiveness of the proposed TBi-YOLOv5. Compared with YOLOv5s, the mean average precision (mAP) of TBi-YOLOv5 has been increased by nearly 4%.