A High-Precision Loose Strands Diagnosis Approach for Isoelectric Line in High-Speed Railway

The isoelectric line is an important component that connects the steady arm and the drop bracket of catenary in high-speed railway. The loose strands of isoelectric line can be commonly observed in real-life applications. In this paper, an automatic fault detection system for the loose strands of the isoelectric line is proposed. This system consists of three stages. First, a convolutional neural network is adopted to extract the isoelectric line features. To accurately and quickly learn these features, an improved feature extraction network, called as the isoelectric line network, is presented. Using the images captured from catenary inspection vehicles, the image areas that contain the isoelectric lines are obtained based on the Faster region-based convolutional neural network. Second, the image segmentation is carried out based on the Markov random field model. And, the accurate isoelectric line pixels are obtained from the smallest image area extracted from the first stage. In the final stage, the fault state is given by analyzing the quantity of the independent connection regions and the pixels' standard deviation. Experimental results show that the proposed system has a high detection accuracy. Furthermore, compared with the convolutional neural networks (the Simonyan and Zisserman model and the Zeiler and Fergus model) and a typical detection method (Histogram of Oriented Gradient + Support Vector Machine), the proposed network has better performance for the isoelectric line location.