深度学习在绝缘子红外图像异常诊断的应用

绝缘子的红外图像分析一般采用图像处理的方法,易受背景环境和数据量的影响,准确率和效率均较低,本文提出一种深度学习的异常诊断方法,基于改进的Faster R-CNN方法搭建检测网络,开展不同类型的绝缘子测试.研究结果表明:相对于神经网络(Back Propagation,BP)、Faster R-CNN方法,本文方法可高...

Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis

Because of the effects of the background environment and data volume,the accuracy and efficiency of abnormal defects in traditional infrared images of insulators are generally low.In this study,a deep-learning anomaly diagnosis method is proposed.Based on the improved faster region-based convolutional neural network(R-CNN)method,a detection network is built to test different types of insulators.Results show that compared with the back propagation neural network and faster R-CNN methods,the proposed method can diagnose abnormal defects of insulators efficiently with a mean average precision of 90.2%.In addition,the diagnostic accuracy of single type I and type V insulators is higher than that of double type I insulators.The results can provide a reference for insulator defect identification in transmission lines.