Detection of tunnel leakage based on gray scale image of laser point cloud

doi:10.13474/j.cnki.11-2246.2023.0228

Abstract

Abstract: Water leakage detection of shield tunnel is the basis of tunnel maintenance. Aiming at the problem that traditional detection methods are difficult to obtain high-quality disease pictures under dim light conditions inside the tunnel, this paper uses lidar to detect internal water leakage in the tunnel. Firstly, based on the characteristics of water leakage disease in the grayscale image of laser point cloud, a data set of water leakage disease in the grayscale image of laser point cloud was established. Then, the Mask R-CNN (Region-CNN) model is used as the benchmark framework, and the Swin transformer network is used as the underlying feature extraction network to realize the rapid detection of tunnel water leakage diseases. Finally, the data collected by the railway mobile measurement system in Wuhan are used for experimental verification. The experimental results show that the detection accuracy of the improved Mask R-CNN model proposed in this paper is higher than that of the original algorithm by more than 12%, and it has good performance in the detection of subway tunnel water leakage diseases.

Key words: metro shield tunnel, leakage, deep learning, disease detection, Swin transformer

CLC Number:

P237

ZHOU Baoding, XIE Peiyao, GUO Wenhao, MAO Qingzhou. Detection of tunnel leakage based on gray scale image of laser point cloud[J]. Bulletin of Surveying and Mapping, 2023, 0(8): 34-39,90.

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