Intelligent Railway Track Fault Detection Using Image Processing and Fuzzy Logic for Enhanced Safety
Authors:-Mrs. G.Tejasri Devi, P.H. Naga Datta Sanjeev, A.Kasi Viswanadh, A.Sankar, P.Veera Mahesh, Y.Lakshmi Chakradhar
Abstract-The advancement of railway transportation vehicles significantly affects the transportation network. Various errors occur due to the utilization of train lines, arising from both manufacturing defects and improper rail usage. Early detection and correction of these faults are crucial, and several techniques have been developed to address this issue. One effective method involves the use of camera-based systems. By employing cameras mounted on railway vehicles, images of rail components are captured and analysed to identify potential defects. This paper proposes a method for detecting and classifying defects on rail track surfaces using image processing techniques. The system relies on high-resolution images obtained from specialized cameras installed on railway inspection vehicles. These images are analysed to identify and assess various track anomalies, including cracks, welding defects, track misalignment, and ballast deterioration. The image processing workflow involves pre-processing, feature extraction, and segmentation to isolate the rail area and detect potential faults. To prioritize maintenance activities, fuzzy logic is applied after identifying and evaluating the severity of defects. This approach is particularly effective in handling the uncertainty and imprecision associated with track condition assessments. Fuzzy rules and membership functions are designed to assign severity levels to the extracted features of each defect category. This method offers a comprehensive and adaptable solution for improving railway track maintenance and ensuring operational safety.
