AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS

12 citations as recorded by crossref.

1. Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution D. Xue & W. Yuan https://doi.org/10.3390/s20143973
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5. Visual Simultaneous Localization and Mapping with Applications to Monitoring of Underground Transportation Infrastructure F. Daneshgaran et al. https://doi.org/10.1177/0361198120963109
6. High-speed acquisition and intelligent tunnel surface defects recognition Y. Wang et al. https://doi.org/10.1016/j.tust.2023.105572
7. Deep learning-based siltation image recognition of water conveyance tunnels using underwater robot X. Wu & J. Li https://doi.org/10.1007/s13349-023-00754-w
8. Innovative synthetic data augmentation for dam crack detection, segmentation, and quantification J. Xu et al. https://doi.org/10.1177/14759217221122318
9. AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation M. Mohammadzadeh et al. https://doi.org/10.3390/automation5040033
10. Detection of Defects Method Using Hammering Robot Imitating Sounds of Workers Y. Takahashi et al. https://doi.org/10.7210/jrsj.38.113
11. Deep-learning-based road crack detection frameworks for dashcam-captured images under different illumination conditions D. Chen & W. Chiu https://doi.org/10.1007/s00500-023-08738-0
12. A coupled 3D laser scanning and digital image correlation system for geometry acquisition and deformation monitoring of a railway tunnel B. Farahani et al. https://doi.org/10.1016/j.tust.2019.102995