Classification of Rail Track Defects Based on Computer Vision Using DNN

Abstract

Abstract: Economic status of the country depends on the Trading which needs transportation. Railways is the most preferred road transportation as most of the profit oriented and movement of people in India is done by elevated railway. Hence it is required to monitor the track health condition frequently using an automated crack detection system. The proposed framework focuses on implementation of python to detect track defects based on Computer vision using image processing techniques. The proposed work uses CNN algorithm through yolov5 model. Yolov5 is one of the best model to achieve highest accuracy in object detection. Yolov5 has become industry standard for object detection due to its speed and accuracy. Here feeding of preprocessed image to CNN classifier to obtain the type of track. The proposed work also helps to identify the severity and nonseverity of defects, also suggests the precautions. Automatic communication occur where the message is sentto authorized people of railway department. The Accuracyof the proposed work on the procured images is more than 95%.