An insulator inspection method based on deep learning applicable to multi‐scale and occlusion conditions

Abstract

As an important equipment of power system, the insulator's normal operation is the basis to ensure the safe operation of the power system. The insulator positioning and identification technology based on machine vision can quickly and accurately complete the inspection of insulators on site and effectively save the cost of operation and maintenance. This paper proposes an insulator inspection method based on region-convolutional neural networks (RCNNs). First, the dataset of the insulator image is preprocessed by means of data expansion. Then, the feature extraction of the insulator image is realised by using the zeiler fergus (ZF) network. The k-means clustering method is used to optimise the selection of anchor points. Meanwhile, the non-maximum suppression post-processing algorithm is improved, and a non-linear penalty factor is introduced to adapt to multi-scale and overlapping occlusion insulator inspection. Experimental results show that the improved faster RCNNs insulator inspection method can accurately obtain the coordinate frame and the corresponding probability value of the insulator object and improve the average precision by 10.43%, achieving the accurate inspection of the insulator object.