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投稿时间：2020-10-10
投稿时间：2020-10-10
中文摘要: 电力设备的安全运行是保证电力系统长期稳定工作的重要基础，因此需要对电力设备的运行状态进行实时监测。实现对电力设备实时监测的关键是对电力设备进行准确的识别和定位。传统的图像检测算法受环境和复杂背景的影响，无法对电力设备进行准确的定位和识别。基于深度学习的目标检测在电力设备运行状态实时监测中具有更广阔的发展前景。针对电力设备红外图像的识别提出了基于Faster R-CNN识别方法。实验结果表明，该方法准确率高，能够准确定位和识别红外图像中的电力设备。
Abstract:The safe operation of power equipment is an important basis to ensure that the power system can work stably for a long time.Therefore, real-time monitoring of the operating status of power equipment is required.The key to real-time monitoring of power equipment is to accurately identify and locate power equipment.Traditional image detection algorithms are affected by the environment and complex background, and cannot accurately locate and identify power equipment.Target detection based on deep learning has broader development prospects in the real-time monitoring of power equipment operating status.The identification method based on Faster R-CNN is launched for the recognition of infrared images of power equipment.Experimental results show that the method has high accuracy and can accurately locate and identify electrical equipment in infrared images.
文章编号：20213002 中图分类号：TM726 文献标志码：
基金项目:
引用文本：
陈鹏,秦伦明.基于深度学习的电力设备红外图像识别[J].上海电力大学学报,2021,37(3):217-220,230.
CHEN Peng,QIN Lunming.Infrared Image Recognition of Power Equipment Based on Deep Learning[J].Journal of Shanghai University of Electric Power,2021,37(3):217-220,230.
陈鹏,秦伦明.基于深度学习的电力设备红外图像识别[J].上海电力大学学报,2021,37(3):217-220,230.
CHEN Peng,QIN Lunming.Infrared Image Recognition of Power Equipment Based on Deep Learning[J].Journal of Shanghai University of Electric Power,2021,37(3):217-220,230.