Automatic Identification and Early Warning Technology of Non-Stop Operation Behavior Risks in Distribution Network Based on Machine Vision

With the widespread application of non-stop power distribution network operations, traditional manual inspection and manual monitoring methods can no longer meet the needs of efficient and accurate safety assurance. To address this problem, this study proposes an automatic risk identification and early warning technology for non-stop power distribution network operations based on machine vision. Specifically, the YOLOv5 (You Only Look Once version 5) algorithm is used to detect the targets of workers and safety protection equipment, and to identify the wearing status of workers' safety protection equipment in real time. The U-Net image segmentation algorithm is then used to accurately identify the insulating shields and safety areas in the working environment. Next, multi-task learning and data enhancement methods are used to improve the robustness and recognition accuracy of the model under different lighting and scenes. Finally, through the integration of video surveillance and risk warning modules, combined with virtual electronic fence technology, the system can automatically identify the illegal behavior of operators and trigger immediate alarms. Experimental results show that the accuracy of the YOLOv5 model can reach up to 93% under strong light conditions; the U-Net model can effectively detect and identify non-compliant occlusion operations when detecting obstructions; and the accuracy of out-of-bounds behavior detection in simple daytime scenarios can reach up to 95%. From the above data conclusions, the combination of YOLOv5 algorithm and U-Net algorithm can not only automatically identify security risks, but also provide real-time and accurate early warning support in complex environments, which has broad application prospects and promotion value.