Train Track Crack Classification Using CNN

Abstract

This research proposes a railway crack detection system. This research describes a classification system that can classify any crack in the railway tracks by using deep learning with convolutional neural networks (CNN). In the railway network of the Indian railways, accidents are one of the major concerns due to the unidentified cracks that are available on the rail tracks. The majority of accidents occur due to railway track cracks, resulting in the loss of precious lives and economic loss. So, it has become necessary to monitor the health condition of the track regularly by using a train track crack classification system. This project prevents the train derailment by classifying cracks in the railway tracks using image processing technologies. To identify the train track crack classification system that uses deep learning with Convolutional Neural Network architecture of different layers along with certain image pre-processing methods has been very successful in the classification of railway track crack has occurred or not. In convolutional neural network, there are a lot of layers available where training of the images are done which are available in the dataset and these layers are made up of lots of neurons. So its have been found that these convolutional neural networks are considered to be able to record the colours and textures of lesions related to corresponding railway track cracks, which is similar to human decision-making.