Abstract:

Foreign objects in transmission line corridors pose risks such as circuit tripping and short circuits. Edge-based real-time foreign object detection is constrained by computing power, power consumption, and latency. Simply scaling up model size does not offer a viable solution under these constraints. Consequently, it is necessary to leverage prior knowledge of specific scenarios under limited resource constraints. Based on the YOLO26n object detection model, this paper introduces auxiliary supervision of tower and conductor structures during the training phase, while removing the auxiliary branches during inference, thereby improving localization quality without additional inference cost. The experimental results demonstrate that this method significantly improves the detection gains for structure-related targets, such as the AP@0.5:0.95 for kites can be increased to 0.864 5, and the average IoU can be improved from 0.897 to 0.929. Based on the experimental results, this study proposes a“positional correlation + visual independence”applicability framework and a differentiated deployment strategy. In low-risk scenarios, accuracy-prioritized models are applied to reduce false alarms, and in high-risk scenarios, recall-prioritized models are adopted to guarantee comprehensive hazard detection, thereby providing effective support for model selection of AI terminals applied in transmission scenarios.

Key words: power transmission safety, AI terminal, foreign object detection, edge intelligence, auxiliary supervision, differentiated deployment