摘要
红外热像仪具有高精度、非接触等特点,在电力系统设备检测中得到了广泛的应用。检测热图像中的设备是自动检测和诊断的基础。为此,本文提出了一种基于深度学习的设备元件实时检测方法,采用一个深度卷积神经网络来预测每个设备部件的坐标、方位角和类别类型。为了提高预测结果的准确性,在模型中加入了零件间方向一致性的先验知识。为了进行评价,构造了一个包含各种场景的大图像集,实验结果表明,该方法对噪声具有很强的鲁棒性,当过并集阈值为0.5时,平均精度达到93.7%。
Infrared thermal imager has the characteristics of high precision and non-contact,which has been widely used in power system equipment fault detection.The equipment of detecting thermal image is the basis of automatic detection and diagnosis.For this reason,a real-time detection method based on deep learning is proposed in this paper.A deep convolution neural network is used to predict the coordinates,azimuth and category types of each equipment component.In order to improve the accuracy of prediction results,a priori knowledge of direction consistency between parts is added to the model.In order to evaluate,a large image set including various scenes is constructed.The experimental results show that the method is very robust to noise.When the over union threshold is 0.5,the average accuracy is 93.7%.
作者
霍成军
史奕龙
武晓磊
李俊午
陆鑫
陈婧
HUO Cheng-jun;SHI Yi-long;WU Xiao-lei;LI Jun-wu;LU Xin;CHEN Jing(State Grid Shanxi Electric Power Company,Taiyuan 030001,China;SGIT-Great Power,Fuzhou 350003,China)
出处
《激光与红外》
CAS
CSCD
北大核心
2021年第4期530-536,共7页
Laser & Infrared
关键词
电气设备检测
深度学习
自动诊断
热像图
electrical equipment detection
deep learning
automatic diagnosis
thermal image
