Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
3D reconstruction and automatic leakage defect quantification of metro tunnel based on SfM-Deep learning method
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.
3D reconstruction and automatic leakage defect quantification of metro tunnel based on SfM-Deep learning method
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.
3D reconstruction and automatic leakage defect quantification of metro tunnel based on SfM-Deep learning method
Yadong Xue (Autor:in) / Peizhe Shi (Autor:in) / Fei Jia (Autor:in) / Hongwei Huang (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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Deep learning based image recognition for crack and leakage defects of metro shield tunnel
| British Library Online Contents | 2018
Deep learning based image recognition for crack and leakage defects of metro shield tunnel
| British Library Online Contents | 2018