TLAD-YOLO: Lightweight network for intelligent detection of railway tunnel lining anomalies using ground penetrating radar

Abstract

Ground Penetrating Radar (GPR) B-scan images and the you only look once (YOLO) series are widely used for tunnel lining intelligent inspections to ensure quality. However, in practical applications, lightweight YOLO detection networks fail to meet the requirements of accuracy and robustness. In view of this, a tunnel lining anomalies detection YOLO (TLAD-YOLO) is proposed for the intelligent detection of railway tunnel lining anomalies based on GPR B-scan images. TLAD-YOLO introduces lightweight spatial and channel synergistic multi-shape attention (SCSMSA) to enhance the detection accuracy of complex scenes and multi-size abnormal objects, while ghost convolution is used to reduce parameters and computation. The experiments are conducted on a dataset consisting of 47 railway tunnels. Furthermore, we propose a multi-scale data augmentation to further expand the dataset, which improves the detection accuracy. The experimental results demonstrate that TLAD-YOLO is an accurate and lightweight detection network, outperforming SOTA detection networks in non-destructive testing of railway tunnels. On the tunnel engineering verification platform and newly built railway tunnels, TLAD-YOLO demonstrates remarkable robustness.