基于图像识别技术的输电线路智能监控系统应用

针对持续多发的输电线路外力破坏事件，人工巡视以及传统监控设备并不能及时有效发现事故隐患，因此提出基于图像识别技术的输电线路智能监控系统.该系统应用卷积神经网络的深度学习算法训练模型，可以智能识别出输电线路现场的安全隐患.建立起前端采集图像，数据无线传输，后台识别分析，隐患定向推送的智能监控新模式.在佛山地区应用实践中，该系统实现了对输电线路现场的24小时实时监控预警，提高了对外力破坏隐患的监管能力，有效预防了大型施工机械所致的线路跳闸事故.

Application of Intelligent Monitoring System for Transmission Lines Based on Image Recognition Technology

In view of the frequent external force damage incidents of transmission lines, manual inspection and traditional monitoring equipment cannot find the hidden dangers in time and effectively. Therefore, an intelligent monitoring system for transmission lines based on image recognition technology is proposed. The system uses convolution neural network depth learning algorithm to train the model, which can intelligently identify the potential safety hazards of transmission lines. A new intelligent monitoring mode is established, which includes front-end image acquisition, wireless data transmission, background recognition and analysis, and hidden danger directional push. In Foshan area, the system realizes 24-hour real-time monitoring and early warning of transmission lines, improves the monitoring ability of hidden dangers caused by external forces, and effectively prevents line tripping accidents caused by large-scale construction machinery.