Enhanced Railway Track Defect Detection Using YOLOBased Deep Learning

Process that is highly prone to errors, thereby escalating the safety threats in railway systems. In this scenario, this project has existed with an automated approach that is utilized to detect defects on railway tracks with the aid of a successful YOLO-based model. The creation of this system is done with a purpose of identifying defects in the railway tracks with the aid of an image dataset utilized during the model training, and in this case, YOLO with a strong robust model is employed, which efficiently predicts defects like cracks on the rail track, though in this case, defects of lesser size are detected. In the rail track alignment can be effectively captured. It is a good example of application in the railway sector, which is in need of modern technologies to increase safety and security.