GEPT-Net An efficient geometry enhanced point transformer for shield tunnel leakage segmentation

Subway shield tunnels have emerged as the preferred solution for urban transportation due to their convenience and safety. Constructed using prefabricated concrete segments, these tunnels exhibit structural stability. However, the segment joints and bolt holes are prone to groundwater infiltration under prolonged external stress, potentially compromising the lifespan of the shield tunnels. Consequently, effective detection methods are imperative to ensure the safe operation of these tunnels. Accurate data acquisition and precise extraction of leakage features are critical for detecting leakages in subway tunnels. This research introduces Efficient Geometry Enhanced Point Transformer Network (GEPT-Net), an innovative point cloud semantic segmentation network designed specifically for detecting tunnel leakage. GEPT-Net leverages the observation that leakages predominantly occur at segment joints and bolt holes, characterized by distinct geometric features and lower intensity. The network incorporates Fast Point Feature Histograms (FPFH) to effectively capture these geometric features from the input data. Additionally, we introduce a point cloud serialization technique utilizing space-filling curves, enabling the network to perceive a greater number of points within the same computational power, thereby balancing efficiency and accuracy. The Geometry Enhanced Channel Attention (GECA) Block is introduced to enhance the interaction between FPFH feature channels and intensity channels, enhancing the precise localization of leakage areas. Furthermore, the Lovasz Hinge Loss is employed to address the issue of extreme class imbalance. We constructed a tunnel leakage point cloud dataset, named S3DIS_leakage, comprising approximately 1,600 m between two stations, to train and evaluate the performance of our network. Experimental results demonstrate that GEPT-Net achieves superior performance in tunnel leakage semantic segmentation, attaining approximately 85 % mean Intersection over Union and 89 % accuracy for leakage classes, outperforming cutting-edge 2D and 3D networks by at least 12 %. Moreover, GEPT-Net maintains a remarkable balance between segmentation accuracy and computational efficiency, rendering it viable for practical engineering applications. This study not only establishes a robust approach for tunnel leakage detection but also paves the way for future research on the comprehensive segmentation of shield tunnel components. The proposed framework is available from the following github repository: https://github.com/jdjiang312/GEPT-Net.