Improving Train Safety: Computer Vision-Based Error Detection for Precise Upkeep

Accidents on railroad tracks continue even with human inspections, which are often inaccurate and can lead to serious incidents. While artificial intelligence has found use in the railway sector, computer vision-based defect diagnosis has received less academic attention. In India and across the world, train travel is the most popular form of transportation because to its reasonable prices and comfortable accommodations for travelers. India possesses one of the largest rail networks globally, and it bears primary responsibility for maintaining these lines in excellent condition. One aspect of the maintenance job includes locating different types of track problems and putting corrective procedures in place for those that are found. This study uses novel data-driven approaches to identify railway track issues utilizing three object recognition models (Yolo series, RCNN, OpenCV, etc.). By detecting fractures, misalignment, loose fasteners, deterioration, and other issues, this model helps reduce incidents involving railway tracks.