基于深度学习的输电线路外破图像识别技术

在电力系统中,识别并排除输电线路外破风险隐患对保障电力系统的安全运行方面具有非常重要的作用.图像识别技术是识别外破风险的一个有效方法.针对外破隐患识别问题,本文提出了一种通过卷积神经网络训练获取深度模型的检测算法,该算法根据防外破风险隐患图像特点对现有深度网络结构进行了改进优化,增加ROI池化层并修改了损失函数；采用大量样本训练得到鲁棒模型,测试时对待测图片首先产生候选区域,然后针对各候选区域进行检测识别,达到在复杂背景中检测出外破风险隐患的目的.实验结果说明了本文方法可以有效地识别出输电线路外破隐患.

Image Recognition Technology for Transmission Line External Damage Based on Depth Learning

In the power system, it is very important to identify and eliminate the hidden dangers of transmission lines to ensure the power system''s security. Image recognition technology is an effective method to identify the risk of breaking out. According to the hidden breaking danger recognition problem, this study proposes a depth model by training the convolutional neural network algorithm. According to the anti breaking characteristics of risk image on the existing depth network structure are improved by increasing the ROI pool layer and modifying the loss function. A large number of training samples are used to get the robust model test when the measured image is first in generated candidate region, then the detection and identification for each candidate region are carried out, to detect potential risks to break out in a complex background. The experimental results show that this method can effectively identify the hidden danger of transmission lines.