MOBILE LASER SCAN DATA FOR ROAD SURFACE DAMAGE DETECTION

11 citations as recorded by crossref.

1. Pothole detection in the woods: a deep learning approach for forest road surface monitoring with dashcams M. Hoseini et al. 10.1080/14942119.2023.2290795
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3. Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress M. De Blasiis et al. 10.3390/rs12060942
4. Automated Pothole Condition Assessment in Pavement Using Photogrammetry-Assisted Convolutional Neural Network E. Ranyal et al. 10.2139/ssrn.4105324
5. Extraction of Vector Data of Region and Line-Type Defects on Road Surface from MMS Point Clouds R. HONMA et al. 10.2493/jjspe.89.654
6. LiDAR-Driven Innovations in Pavement Distress Detection: A Review Y. Si et al. 10.1061/JCCEE5.CPENG-6662
7. An extensive bibliometric analysis of pavement deterioration detection using sensors and machine learning: Trends, innovations, and future directions M. Rizelioğlu 10.1016/j.aej.2024.09.097
8. A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change R. Corbally & A. Malekjafarian 10.1016/j.engstruct.2021.113783
9. Automated pothole condition assessment in pavement using photogrammetry-assisted convolutional neural network E. Ranyal et al. 10.1080/10298436.2023.2183401
10. Relative Flatness Analysis in Mobile Pavement Condition Monitoring Using Hybrid Solid-State LiDAR T. Chuang & Y. Chang 10.1061/JCCEE5.CPENG-6264
11. Bridge roughness scanned by Dual-Wheeled 3D test vehicle and processed by augmented Kalman filter: Theory and application Z. Li et al. 10.1016/j.compstruc.2024.107581