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

Abstract

Railway track inspection plays a vital role in maintaining transportation safety, operational efficiency, and infrastructure reliability. Conventional manual inspection techniques are labor-intensive, expensive, and susceptible to human errors. This paper proposes an advanced deep learning-based framework for automated railway track defect detection using Region Convolutional Neural Networks (RCNN). The proposed approach utilizes images acquired from a rolling camera installed beneath a moving inspection vehicle. The captured images are subjected to preprocessing and feature extraction procedures to improve defect detection performance. A hybrid combination of Convolutional Neural Networks (CNN) and RCNN is employed to identify and classify railway track defects accurately. The framework effectively differentiates between defective and non-defective track conditions. Experimental evaluation demonstrates that the proposed approach achieves an accuracy exceeding 90%, making it a dependable and efficient solution for real-time railway track inspection and maintenance management.