Abstract: The existing crack detection algorithms for highway tunnels exhibit drawbacks such as inadequate feature extraction and weak antiinterference ability, leading to missed detections and slow detection speeds. Therefore, a tunnel crack detection method based on improved YOLOv5 is proposed. First, the residual module C3SM is introduced into the backbone network along with an efficient threedimensional parameter-free attention mechanism to enhance the interaction between deep and shallow feature information. This enhances the feature extraction capability of the network while optimizing computational complexity. Second, a new feature fusion network structure is employed in the feature pyramid to integrate the feature maps of neighboring layers, better preserving crack edge information and accelerating model detection without loss of semantic information. Finally, a position loss function, WIoU, is used to optimize the detection of occluded and overlapped targets. To validate the effectiveness of this method, extensive experiments are conducted on the Tunnel-crack and Huzhou tunnel crack datasets. The results show that the proposed method achieves 88.4% and 103.5 frames/s and 85.1% and 99.4 frames/s in terms of accuracy and speed, respectively. These results demonstrate a higher crack detection accuracy than most highperformance target detectors.

Key words: tunnel engineering, crack detection, attention mechanism, YOLOv5, WIoU