Towards the development of obstacle detection in railway tracks using thermal imaging

Abstract

To prevent collisions between trains and objects on the railway line, rugged trains require an intelligent rail protection system. To improve railway safety and reduce the number of accidents, studies are underway. Machine learning (ML) had progressed rapidly, creating new perspectives on the subject. A technique called speed up robust features (SURF) is proposed by researchers to collect regionally and globally relevant information. In addition, taking advantage of the Ohio State University (OSU) heat walker benchmarking dataset, the effectiveness of this technique was examined under various lighting scenarios. This technology could help in reducing train accident rates and financial costs. The findings of the proposed methodology are very specific, in addition to the ability to quickly identify items (obstacles) on the railway line, both of which contribute to rail security. The proposed faster region based convolutional neural network (FR-CNN) with 2D singular spectrum analysis (SSA) improves the performance analysis of an accuracy of 90.2%, recall 95.6% and precision 94.6% when compared with an existing system YOLOv2 and YOLOv5 with 2D SSA.