Adaptive UAV Inspection and Greedy Algorithm–Based Path Planning for Efficient Roof Anomaly Detection

Abstract

Unmanned aerial vehicle (UAV) roof inspections are often limited by restricted flight times and the inefficiency of uniform coverage strategies. This study introduces an adaptive weighted coverage planning framework that prioritizes structurally informative regions using visual cues, bypassing the need for explicit defect annotations. The framework generates a grid-level informativeness map by fusing multiscale keypoint density, seam detection, color consistency, and illumination reliability. To optimize deployment, high-priority regions are aggregated into inspection blocks via a greedy region-merging algorithm, effectively decoupling region selection from path planning. A nearest neighbor (NN) planner + 2-opt refinement is then utilized for real-time trajectory generation and compared against randomized approximate neighbor + 2-opt and simulated annealing methods. Experimental results on high-resolution UAV imagery of concrete/cement roofs demonstrate that the framework concentrates inspection efforts on critical structural areas while suppressing redundant, homogeneous regions. Quantitative analysis shows that NN + 2-opt provides an optimal balance between trajectory quality and computational efficiency. This modular approach offers a scalable, interpretable solution for autonomous infrastructure monitoring, directly addressing the operational constraints of limited UAV flight time and the diagnostic inefficiency of uniform coverage strategies in large-scale facility inspection.