Railway Track Fault Detection Using Image Processing

Abstract

Railway track faults are a major concern as they can lead to derailments, accidents, and significant losses. Regular track inspections are necessary to ensure safe railway operations. However, manual inspections are time-consuming, labor-intensive, and prone to human errors. To overcome these challenges, this project proposes an automated railway track fault detection system using image processing techniques.The system captures images of railway tracks using a mounted camera, which continuously records the track’s condition as a train or inspection vehicle moves along the railway line. These images are then processed using advanced image processing algorithms to detect cracks, breaks, and misalignments in the tracks. Techniques such as edge detection, thresholding, and morphological operations are employed to enhance the visibility of track faults. The system uses machine learning-based classification to differentiate between normal and faulty track sections, ensuring high accuracy in fault identification. By automating the detection process, this approach significantly reduces the time and effort required for railway maintenance. It provides a more reliable and efficient method for detecting faults, minimizing the risk of accidents and ensuring safer railway operations. Additionally, real-time alerts can be generated to notify railway authorities about detected faults, enabling quick repairs and preventing potential disasters. This project demonstrates the effectiveness of image processing in railway track inspection, offering a cost-effective and scalable solution for railway safety and maintenance. By using this automated method, railway authorities can identify track faults faster and take quick action to fix them. This improves railway safety, reduces maintenance costs, and prevents accidents. The project demonstrates how image processing can provide a simple, fast, and reliable way to keep railway tracks in good condition.