An Autonomous UAV Power Inspection Framework with Vision-Based Waypoint Generation

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, it plays an increasingly important role in electrical power inspection. Automated approaches that generate inspection waypoints based on tower features have emerged in recent years. However, these solutions commonly rely on tower coordinates, which can be difficult to obtain at times. To address this issue, this study presents an autonomous inspection waypoint generation method based on object detection. The main contributions are as follows: (1) After acquiring and constructing the distribution tower dataset, we propose a lightweight object detector based on You Only Look Once (YOLOv8). The model integrates the Generalized Efficient Layer Aggregation Network (GELAN) module in the backbone to reduce model parameters and incorporates Powerful Intersection over Union (PIoU) to enhance the accuracy of bounding box regression. (2) Based on detection results, a three-stage waypoint generator is designed: Stage 1 estimates the initial tower’s coordinates and altitude; Stage 2 refines these estimates; and Stage 3 determines the positions of subsequent towers. The generator ultimately provides the target’s position and heading information, enabling the UAV to perform inspection maneuvers. Compared to classic models, the proposed model runs at 56 Frames Per Second (FPS) and achieves an approximate 2.1% improvement in mAP50:95. In addition, the proposed waypoint estimator achieves tower position estimation errors within 0.8 m and azimuth angle errors within 0.01 rad. Multiple consecutive tower inspection flights in actual environments further validate the effectiveness of the proposed method. The proposed method’s effectiveness is validated through actual flight tests involving multiple consecutive distribution towers.