UAV-Based Detection of Rock Mass Instabilities Using DBSCAN and 3D Region Growing Algorithms - Chen, Na; Hu, Min; Xiao, Henglin; Li, Yahu; Hou, Zeng (Geotechnical and geological engineering, 2025)
This study presents an unmanned aerial vehicle (UAV) photogrammetry system that integrates DBSCAN clustering and 3D region growing algorithms to detect unstable rocks on high-angle slopes (50°–80°), addressing critical safety challenges in geotechnical monitoring. The system was applied at China’s Yulong Kashi Hydropower Station, where it combined Alpha-shape algorithms with polyhedral projection integration to quantitatively analyze 3D rock mass characteristics, including backwall inclination (28.54°–55.2°), area (96.01–17,892.27 m 2 ), volume (320.49–174,964.55 m 3 ), and elevation difference (3.71–27.88 m). Stability assessments based on safety factor (K-value) and volumetric classification identified one relatively stable rock mass (LA6) and nine unstable ones, with LB1 being the most severe due to its exceptionally large volume (174,964.55 m 3 ) requiring urgent controlled blasting. The system demonstrated significant efficiency improvements, achieving a 71.8% reduction in processing time compared to manual methods (2.8–5.0 s vs. 8.9–20.2 s per case) and 83% lower time variability, particularly excelling in complex scenarios (e.g., saving 16.9 s for LA14). By combining advanced algorithms with 3D analytics, this approach provides a robust, rapid tool for risk mitigation on steep slopes, offering a new standard for geotechnical monitoring in challenging environments.