OBJECT-BASED AND SUPERVISED DETECTION OF POTHOLES AND CRACKS FROM THE PAVEMENT IMAGES ACQUIRED BY UAV

22 citations as recorded by crossref.

1. Automatic Defect Detection of Pavement Diseases L. Zhao et al. https://doi.org/10.3390/rs14194836
2. A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress Z. Lv et al. https://doi.org/10.3390/app15116112
3. Exploring Smart Tires as a Tool to Assist Safe Driving and Monitor Tire–Road Friction M. Pomoni https://doi.org/10.3390/vehicles4030042
4. A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices M. Solla et al. https://doi.org/10.3390/rs13040672
5. An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection W. Tang et al. https://doi.org/10.1109/TITS.2021.3084809
6. Airport Pavement Distress Analysis H. Noori & R. Sarkar https://doi.org/10.1007/s40996-023-01240-5
7. Deep Domain Adaptation for Pavement Crack Detection H. Liu et al. https://doi.org/10.1109/TITS.2022.3225212
8. PDS-UAV: A Deep Learning-Based Pothole Detection System Using Unmanned Aerial Vehicle Images O. Alzamzami et al. https://doi.org/10.3390/su16219168
9. Reconstructing Road Roughness Profiles Using ANNs and Dynamic Vehicle Accelerations K. Douier et al. https://doi.org/10.3390/infrastructures9110198
10. Sustainable road infrastructure operation via intelligent UAV inspection systems: Trends, challenges, and research opportunities W. Li et al. https://doi.org/10.1016/j.autcon.2026.106909
11. Modified YOLOv8 for automated detection and identification towards highway pavement distress Z. Hao et al. https://doi.org/10.1016/j.conbuildmat.2025.143428
12. Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild S. Huang et al. https://doi.org/10.1109/TITS.2023.3245192
13. Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs) I. Feitosa et al. https://doi.org/10.3390/su16052207
14. Machine learning techniques for pavement condition evaluation N. Sholevar et al. https://doi.org/10.1016/j.autcon.2022.104190
15. Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review T. Yee et al. https://doi.org/10.1109/ACCESS.2022.3197291
16. Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution D. Xue & W. Yuan https://doi.org/10.3390/s20143973
17. PotSpot: Participatory sensing based monitoring system for pothole detection using deep learning S. Patra et al. https://doi.org/10.1007/s11042-021-10874-4
18. Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review E. Ranyal et al. https://doi.org/10.3390/s22083044
19. Review and Insights Toward Cognitive Digital Twins in Pavement Assets for Construction 5.0 M. Oditallah et al. https://doi.org/10.3390/infrastructures10030064
20. Automated detection of airfield pavement damages: an efficient light-weight algorithm H. Liang et al. https://doi.org/10.1080/10298436.2023.2247135
21. Smart monitoring of road pavement deformations from UAV images by using machine learning H. Ibrahim et al. https://doi.org/10.1007/s41062-023-01315-2
22. Specification of Forest Road Surface Potholes using UAVs Image Evaluation A. rezaei mutlagh et al. https://doi.org/10.61186/ifej.11.22.110