Visual-Based Dual Detection and Route Planning Method for UAV Autonomous Inspection

The intelligent development of unmanned aerial vehicles (UAVs) will make power inspection work more convenient. However, challenges such as reliance on precise tower coordinates and the low accuracy in recognizing small targets limit its further development. In this regard, this study proposes an autonomous inspection method based on target detection, encompassing both flight route planning and defect detection. For route planning, the YOLOv8 model is lightly modified by incorporating the VanillaBlock module, the GSConv module, and structured pruning techniques to enable real-time tower detection. Based on the detection results and UAV states, an adaptive route planning strategy is then developed, effectively mitigating the dependence on predefined tower coordinates. For defect detection, the YOLOv8 model is further enhanced by introducing the SPD-Conv module, the CBAM, and the BiFPN multi-scale feature fusion network to improve detection performance for small targets. Compared with multiple baseline models, the computational cost of the improved lightweight model is reduced by 23.5%, while the detection accuracy is increased by 4.5%. Flight experiments further validate the effectiveness of the proposed route planning approach. The proposed fully autonomous inspection method provides valuable insights into enhancing the autonomy and intelligence of UAV-based power inspection systems.