An improved block-level approach for tunnel lining crack detection

Abstract

Abstract Tunnel lining cracks pose a great safety risk to safe operation. Due to severe illumination conditions and lining cracks, which are usually thinner with varied shapes compared to cracks generated in pavement, bridge, and other transportation infrastructure, the lining crack inspection at the block level is a challenging task. In this study, to address this issue, we propose a novel Swin Transformer-based network including Shape-IoU method to enhance the perception of the tunnel lining crack and the refinement of the crack. To validate the superiority and robustness of the proposed model, a total of six classic object detection models (i.e. Cascade Mask R-CNN, Cascade R-CNN, Faster R-CNN, feature selective anchor-free module, fully convolutional one-stage object detection, neural architecture search—fully convolutional one-stage object detection) are adopted for model training, validation, and testing with the customized inspection dataset, which includes 1,200 high resolution tunnel lining inspection images. With the training results and the visualization results, our proposed model has shown excellent performance across multiple metrics. Specifically, the enhanced Swin Transformer model achieves a remarkable 96.10 per cent on the mAP50 metric, surpassing the original model by 0.80 per cent. Moreover, it exhibits an accelerated detection speed of 2.4 tasks/s compared to the previous rate of 1.43 tasks/s. The results demonstrate that the proposed methodology in this paper significantly enhances both detection accuracy and speed of the model, paving the way for field application in the near future.