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

机译：基于目标检测的输电线路潜在安全隐患监测

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摘要

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.
机译：输电线路安全监控是维护国家电网安全的重要任务之一。本文采用基于计算机视觉的目标检测方法，对输电线路的安全隐患进行自动监测。我们首先创建一个潜在的安全风险对象数据集。其次，我们分析了大多数最新的对象检测模型。第三，根据特定的数据集，训练了对象检测模型，该模型使用训练技巧来获得高性能。第四，我们建立了将判别结果反馈到显示终端的监控系统，可以全面掌握整个安全区域的情况，确保传输网络的安全运行。我们的实验表明，基于深度学习的Cascade R-CNN检测框架和基于高分辨率表示网络的主干网络的出色结果。它在IOU阈值为0.5的26种对象数据集上获得了81.5 mAP，并显示了基于深度学习的隐藏危险检测算法可以准确地区分危险源。监控系统将判别结果反馈给显示终端，可以全面掌握整个安全区域的情况，确保传输网络的安全运行。

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