Title: A road tunnel detection method based on SE-YOLOv5 network

Authors: Yu Bai; Jichao Wang; Xuewei Zhang; Chaojie Zhang; Huiyuan Liu; Kongyun Chen; Jian Wang

Addresses: China State Construction Engineering Corporation Ltd., Beijing, 100071, China ' China State Construction Engineering Corporation Ltd., Beijing, 100071, China ' China State Construction Engineering Corporation Ltd., Beijing, 100071, China ' School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China ' Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China ' Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China ' Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China

Abstract: 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 Map@0.5 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.

Keywords: structural disease detection of highway tunnels; YOLOv5; object detection; squeeze-and-excitation attention mechanism; heterogeneous data; target detection; BiFPN.

DOI: 10.1504/IJCSM.2025.147463

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.3, pp.179 - 193

Received: 10 Dec 2024
Accepted: 28 Mar 2025
Published online: 16 Jul 2025 *

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