A road tunnel detection method based on SE-YOLOv5 network

This paper proposes the SE-YOLOv5s network model for road tunnel detection, focusing on structural disease detection using a multi-sensor fusion approach. The model fuses multivariate heterogeneous data from multiline LIDAR and monocular cameras. Three key improvements are made to the YOLOv5 network: 1) the SE attention module is integrated into the YOLOv5s backbone to enhance feature extraction while reducing computational cost; 2) the DC-BiFPN feature fusion network replaces the PANet to improve small target detection; and 3) the EIoU loss function is used instead of CIoU to improve detection performance. Experimental results show that the improved SE-YOLOv5s outperforms YOLOv5s, YOLOv3, and YOLOv4, with [email protected] increases of 8.1%, 15.1%, and 10.1%, respectively. The results demonstrate the higher accuracy of the SE-YOLOv5s in detecting structural diseases in road tunnels.