Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection

Abstract

Rod insulators are vital parts of the catenary of high speed railways (HSRs). There are many different catenary insulators, and the background of the insulator image is complicated. It is difficult to recognise insulators and detect defects automatically. In this paper, we propose a catenary intelligent defect detection algorithm based on Mask region-convolutional neural network (R-CNN) and an image processing model. Vertical projection technology is used to achieve single shed positioning and precise cutting of the insulator. Gradient, texture, and gray feature fusion (GTGFF) and a K-means clustering analysis model (KCAM) are proposed to detect broken insulators, dirt, foreign bodies, and flashover. Using this model, insulator recognition and defect detection can achieve a high recall rate and accuracy, and generalized defect detection. The algorithm is tested and verified on a dataset of realistic insulator images, and the accuracy and reliability of the algorithm satisfy current requirements for HSR catenary automatic inspection and intelligent maintenance.