Monitoring of Potential Safety Hazards of Transmission Lines Based on Object Detection

Power transmission line safety monitoring is one of the important tasks to maintain the security of national power grid. In this paper, the object detection method based on computer vision is applied to automatically monitor the potential safety risk of transmission line. We firstly create a potential safety risk object dataset. Secondly we analyze most state-of-the-art object detection model. Thirdly according to the specific dataset, an object detection model was trained, which uses training tricks to get high performance. Fourthly, we built a monitoring system that feeds the discriminant results back to the display terminal, which can comprehensively grasp the situation of the whole safe area and ensure the safe operation of the transmission network. Our experiments show the excellent results are Cascade R-CNN detection framework based on deep learning and backbone based on high resolution representations network. It gains 81.5 mAP on 26 kinds of objects datasets at IOU threshold 0.5, and show hidden danger detection algorithm based on deep learning can accurately discriminate the dangerous sources. The monitoring system feeds the discriminant results back to the display terminal, which can comprehensively grasp the situation of the whole safe area and ensure the safe operation of the transmission network.