An Improved YOLOv5 Crack Detection Method Combined with a Bottleneck Transformer

Abstract

Efficient detection of pavement cracks can effectively prevent traffic accidents and reduce road maintenance costs. In this paper, an improved YOLOv5 network combined with a Bottleneck Transformer is proposed for crack detection, called YOLOv5-CBoT. By combining the CNN and Transformer, YOLOv5-CBoT can better capture long-range dependencies to obtain more global information, so as to adapt to the long-span detection task of cracks. Moreover, the C2f module, which is proposed in the state-of-the-art object detection network YOLOv8, is introduced to further optimize the network by paralleling more gradient flow branches to obtain richer gradient information. The experimental results show that the improved YOLOv5 network has achieved competitive results on RDD2020 dataset, with fewer parameters and lower computational complexity but with higher accuracy and faster inference speed.

Keywords

• YOLOv5
• crack detection
• Bottleneck Transformer
• deep learning