Railways – Rail Track Surface Fault & Defect Detection Based on Deep Learning

Abstract

The contemporary exigency for efficient and meticulous rail-track maintenance within the expansive realm of railway infrastructure necessitates the relentless pursuit of innovative approaches. This research, a harmonious symphony of cutting-edge deep-learning and sophisticated computer-vision, is poised to deliver unprecedented prowess in the detection of hitherto undetected surface faults and defects on rail tracks. Leveraging the transformative capabilities of Region-based Convolution-Neural-Networks (R-CNN), the proposed methodology strives to elucidate heretofore ambiguous cues that herald potential vulnerabilities. The resultant amalgamation of technology and technique promises to redefine the epochal paradigm of rail track maintenance.