Tan, Lei (Beijing Municipal Engineering Research Institute), Zhang, Xiaohan (Zhejiang University), Zeng, Xinrui (Beihang University), Hu, Xiaoxi (Beijing Jiaotong University), CHEN, FEI (Wuhan University), Liu, Zongyang (Huazhong University of Science and Technology), Liu, Jin (University of Leeds), TANG, Tao (Beijing Jiaotong University)

LCJ-Seg: Tunnel Lining Construction Joint Segmentation Via Global Perception

Scheduled for presentation during the Invited Session "Control, Communication and Emerging Technologies in Smart Rail Systems II" (WeBT7), Wednesday, September 25, 2024, 14:50−15:10, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

As railway facilities and equipment maintenance becomes a focal point of current railway research, tunnels, as fundamental components of railway infrastructure, rightly garner attention for intelligent railway maintenance. However, detecting tunnel lining joints poses challenges due to their potential confusion with other internal tunnel objects such as pipelines and cables. To address this issue, we propose an effective segmentation method, LCJ-Seg, leveraging Global Perception Module (GPM) and Detail Preservation and Feature Denoising Module (DPFDM). The GPM is incorporated into the backbone for accurately identifying tunnel lining joints, as these joints require global context to properly distinguish them from other internal tunnel objects. The DPFDM preserves fine details and reduces noise of low-level features to enhance feature fusion. It ensures that both high-level and low-level features retain important details crucial for the lining construction joint segmentation task. Comprehensive comparative experiments and ablation studies on a real collected dataset, demonstrating the superiority of our approach over existing segmentation methods.