Key points are not available for this paper at this time.
The live-line operation of 10 kV distribution networks is critical for ensuring uninterrupted and high-quality power supply. However, operational sites face challenges such as insufficient intelligent monitoring and suboptimal realtime performance. To address these issues, this study proposes the FEM-YOLOv8 algorithm, specifically designed for protective equipment detection in live-line operation scenarios. The proposed algorithm is deployed on edge devices compatible with unmanned aerial vehicles (UAVs), enabling remote, autonomous, and intelligent monitoring. Key improvements include the introduction of an enhanced FAST-C2f module, replacing the original C2f module in the Backbone to improve feature extraction efficiency while reducing model complexity. Additionally, a lightweight efficient channel attention (ECA) mechanism is incorporated into the Backbone and Neck to enhance target feature detection and representation capabilities. The bounding box regression loss function is replaced with metric preserving distance intersection over union (MPDIoU) to further boost detection accuracy and robustness. The FEM-YOLOv8 model is implemented on the Atlas 200I DK A2 edge device, which is suitable for UAV deployment. Experimental results demonstrate that the improved FEM-YOLOv8 model achieves 93.1% precision (P), 85.9% recall (R), and 92.3% mean average precision (mAP), surpassing the baseline model by 2.8, 3.2, and 2.2 percentage points, respectively. With a detection speed of 83 frames per second (FPS) and a power consumption of only 10.2 W, the model satisfies real-time performance and detection accuracy requirements, providing significant contributions to grid intelligence and power operation safety.
Duan et al. (Tue,) studied this question.