A Comprehensive Deep Learning Framework for Railway Track Fault Identification Using Region Convolutional Neural Networks

Abstract

The inspection of railway tracks is extremely essential for the safety, security, efficiency, and smooth functioning of the railway transportation system. Conventional methods of railway track examination rely on manual surveys, which require substantial time, labor, and are subject to human predispositions and inconsistency. This paper presents an intelligent deep learning technique to automatically detect and localize railway track faults through a hybrid Convolutional Neural Network (CNN) and Region-based Convolutional Neural Network (R-CNN) framework. Images of railway tracks, recorded by a railway inspection moving vehicle fitted with an imaging device, undergo a processing pipeline consisting of image enhancement, noise filtering, feature extraction and segmentation, region proposal, and fault classification. The CNN component is employed for feature learning, while the R-CNN component enables accurate defect localization, including rail cracks, irregularities, and alignment defects. The proposed framework is designed to operate reliably under varying environmental conditions, including illumination variations and background complexities. Experimental results indicate that the proposed method achieves high classification accuracy and provides a reliable defect-detection system with a lower false-alarm ratio compared to traditional machine learning and image-processing methods. This literature-based study reviews existing railway track inspection techniques and formulates an integrated CNN–R-CNN framework to support automated, scalable, and safety-critical railway infrastructure monitoring.