TACK PROJECT: TUNNEL AND BRIDGE AUTOMATIC CRACK MONITORING USING DEEP LEARNING AND PHOTOGRAMMETRY

11 citations as recorded by crossref.

1. Low-Light Image Segmentation on Edge Computing System S. Choi & S. Kim https://doi.org/10.3390/s26010327
2. Three-dimensional modeling and defect quantification of existing concrete bridges based on photogrammetry and computer aided design S. Abu Dabous et al. https://doi.org/10.1016/j.asej.2023.102231
3. Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm J. Ma et al. https://doi.org/10.1155/2022/7879302
4. Tunnel scanner: Geometry-informed synthetic point cloud generation and transfer learning for tunnel segmentation W. Yang et al. https://doi.org/10.1016/j.autcon.2026.106924
5. An active learning method for crack detection based on subset searching and weighted sampling Z. Xiang et al. https://doi.org/10.1177/14759217231183661
6. Mapping built environments from UAV imagery: a tutorial on mixed methods of deep learning and GIS X. Hong et al. https://doi.org/10.1007/s43762-022-00039-w
7. On the Design of Permanent Rock Support Using Fibre-Reinforced Shotcrete A. Sjölander et al. https://doi.org/10.3390/fib11020020
8. Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings A. Sjölander et al. https://doi.org/10.3390/s23063189
9. Application of machine learning-based AI in defect monitoring of earth retaining structures and tunnels of transport systems: A review M. Islam et al. https://doi.org/10.1016/j.treng.2025.100385
10. LiDAR-Based Road Cracking Detection: Machine Learning Comparison, Intensity Normalization, and Open-Source WebGIS for Infrastructure Maintenance N. Pascucci et al. https://doi.org/10.3390/rs17091543
11. Crack Monitoring from Motion (CMfM): Crack detection and measurement using cameras with non-fixed positions V. Belloni et al. https://doi.org/10.1016/j.autcon.2023.105072