3D reconstruction and automatic leakage defect quantification of metro tunnel based on SfM-Deep learning method

Abstract

Various structural defects deteriorate tunnel operation status and threaten public safety. Current tunnel inspection methods face problems of low efficiency, high equipment expense, and difficult data management. Combining the deep learning model and the 3D reconstruction method based on structure from motion (SfM), this paper proposes a novel SfM-Deep learning method for tunnel inspection. The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction. The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model. The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects. By projecting the model to the design cylindrical surface and expanding it, the tunnel leakage area is quantified. Through its practical application in a Shanghai metro shield tunnel, the reliability of the proposed method was verified. The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.

Keywords

• 3D reconstruction
• Leakage defect quantification
• Visualization
• Metro tunnel
• Structure from motion
• Deep learning