ABSTRACT
Recently, computer vision (CV)-based techniques have been widely applied for inspecting shield tunnel lining defects. However, this inspection method will produce a lot of image data. How to efficiently detect water leakage from the large image database therefore becomes a new challenge. To effectively improve recognition of water leakage of metro shield tunnel lining, a Mask Region-based Convolutional Neural Network (Mask R-CNN)-based method is employed in this paper. This approach efficiently detects water leakage in an image while simultaneously generating a high-quality segmentation mask for each water leakage. To realize the proposed method, a database including 4525 images is developed. Then, the Mask R-CNN is modified and trained using this database to detect and segment water leakage in images. The segmentation results show that the trained model shows quite better performances and can rapidly and accurately recognize water leakage of metro shield tunnels.
