Automated Railway Track Fault Detection using ResNet50V2 Deep Learning Architecture

In this study on the use of ResNet50V2 deep learning architecture for automatic detection of railway track defects from images. It will bring about new approaches to meet the urgent need for rapid and accurate fault systems which guarantee the safety and reliability of railway operation. Using the recent developments in deep learning, particularly the ResNet50V2 model, this work presents a method to distinguish between two kinds of railway track faulty images. This approach uses a dataset of 384 images, with 30 iterations in training and batch size of 16. The default rates for learning are used in this analysis. Through careful testing, the paper demonstrates the ability of the ResNet50V2 model to distinguish railway track faults accurately, It achieves an accuracy rate of 86%. The study discovers the possibility of deep learning models such as ResNet50V2 enhancing efficiency and reliability for railway track fault detection systems. The successful realization of this approach will have significant impacts on railway maintenance processes. It will allow problems to be identified early and allocated with in time to prevent accidents and delays, a long-term effect which grows worse as time goes on. In addition, the study shows the availability of advanced machine learning techniques pose for tackling crucial problems in railway infrastructure management. This will cover the way towards transportation networks that are safer and more robust.