Power Line Structure Reconstruction and Detection Method Based on Global Regression of Keypoints

Unmanned aerial vehicle (UAV) optical remote sensing provides a flexible means of mapping and inspecting power-transmission corridors. However, the automatic extraction of power lines from aerial imagery remains challenging because conductors have slender geometry, weak texture, low contrast, and discontinuous visibility and are easily affected by imaging noise, occlusion, and complex background interference. Consequently, segmentation-based methods often produce fragmented masks that limit the reliability of subsequent structural reconstruction. This study presents a power line detection and reconstruction framework based on global keypoint regression for UAV optical imagery. Rather than recursively associating adjacent keypoints, the framework regresses every keypoint belonging to the same power line toward a common start point. This shared reference is designed to avoid recursive propagation through a chain of local associations. Three complementary components are incorporated: a multi-scale region-aware feature extractor (MSRAFE) for combining local and global semantics across scales, a line feature aggregation (LFA) module for adaptive multi-level feature fusion, and a linear structure-aware feature strategy (LSAFS) for coupling local geometric cues with global structural relationships. The method was evaluated on two public aerial-image benchmarks and a self-constructed dataset. It achieved intersection-over-union (IoU) and F1 scores of 85.76% and 91.76% on PLDU and 75.23% and 83.95% on TTPLA, respectively. On the self-constructed CPLD dataset, it obtained the highest IoU (70.44%) and precision (83.87%) among the compared methods. These pixel-level results support the performance of the complete framework for detecting thin linear objects in airborne optical remote sensing imagery.