Work

Railway tunnel lining structural damage detection algorithm based on improved YOLOv5s

Year: 2025

Type: conference-paper

Abstract: Addressing various defects in the internal structure of operational railway tunnels, such as difficulties in discovering, detecting, and identifying defects like uncompacted, hole, and void, propose the CCB-YOLOv5 algorithm for detecting defects in railway tunnel lining structures. The algorithm is based on the YOLOv5 framework and incorporates the ConvNeXt feature enhancement module into the Backbone network, improving the model's ability to extract features from fine textures of lining defects. Add the Coordinate Attention (CA) module after upsampling in the feature pyramid network of the Neck section to enhance defect feature information and reduce background interference. Introduce the Bidirectional Feature Pyramid Network (BiFPN) to fuse shallow layer defect detail textures, reducing the loss of deep network detail features during sampling. To evaluate the performance of the proposed algorithm, it was compared with SSD, Faster RCNN, YOLOv5m, YOLOv8s, and YOLOv8n. The experimental results indicate that the improved algorithm achieved 92.3% precision (P) and 94.1% mean average precision (mAP) in detecting structural defects in railway tunnel linings, outperforming the other mainstream algorithms in terms of recognition accuracy and overall performance.

Source: International Conference on Computer Application and Information Security (ICCAIS)

DOI: 10.1117/12.3062223

Authors: Chengchen Wang, Fei Yuan, Dexiang Zhang, Fuhui Xu

Institutions: Anhui University, Chinese Academy of Sciences, Institute of Information Engineering

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4409784668