Railway Track Monitoring System Using Computer Vision

Railway track monitoring is a critical aspect of railway infrastructure maintenance, ensuring safe and efficient transportation. Traditional inspection methods often involve manual assessments, which can be time-consuming, labor- intensive, and prone to human error. With advancements in computer vision and artificial intelligence, automated railway track monitoring has emerged as a reliable solution to enhance safety and efficiency. This paper presents a comprehensive analysis of a computer vision-based railway track monitoring system that utilizes image processing and machine learning techniques to detect anomalies such as cracks, track misalignment, and obstacles. The study explores various methodologies employed in automated track inspection, highlighting the significance of real-time monitoring and data- driven decision-making. Additionally, this research examines the role of deep learning models in improving defect detection accuracy and reducing maintenance costs. By integrating smart surveillance and AI-driven analytics, the proposed system aims to optimize railway infrastructure management, ensuring enhanced safety and operational reliability.