Due to the more complex environment,too many influencing factors,and difficult to extract features of draped glass insulators ice-covered glacier,the ice-covered glacier recognition of draped glass insulators is less accurate.For this reason,a method of identification and evaluation of ice-covered glacier for draped glass insulator based on image analysis is proposed.The domain average method is adopted to smooth and denoise the acquired insulator ice-covered glacier images.A convolutional neural network hierarchy is established according to insulator morphology parameters.The imaging features of glacier are captured through the principle of mirror imaging.The imaging size is set according to the field environment parameters.The ice-covered glacier for draped glass insulator is identified through conversion.The risk indicator of glacier is simulated according to historical data,and the weighting parameters are set.The assessment by weight comparison is completed.The experimental results show that the proposed method has a minimum recognition error rate of 7%and a root-mean-square error of 0.15.The images obtained by applying the method are clearer and the detail preservation effect is better.The method can effectively realize the identification of ice-covered glacier for draped glass insulators,and the accuracy of both identification and assessment is high.
关键词
图像分析/悬垂式玻璃绝缘子/平滑窗口/灰度值/卷积神经网络/覆冰线路
Key words
Image analysis/Draped glass insulator/Smoothing window/Gray value/Convolutional neural network/Ice-covered line
维普
万方数据