Efficient quantifying track structure cracks using deep learning

Abstract

High-speed railway ballastless track structure crack detection usually has a high demand for the efficiency of crack detection technology. To overcome the limitation that current crack quantification methods usually require multiple steps, this paper proposes an efficient quantification method for track structure cracks using deep learning. This method applies the deep neural network (DNN) to the direct prediction of crack severity index values by modifying DNNs used for image classification. This method adopts a deep learning-based multi-step crack quantification method to calculate crack severity index values, establishes a dataset for predicting track structure interlayer crack severity index values using crack width mean values as labels, establishes a dataset for predicting track structure complex crack severity index values using crack width mean values and crack area values as labels, and utilizes the established datasets to train the modified DNNs. This method crops the track structure panorama in spatial order to obtain images, which not only facilitates DNN prediction but also enables the acquisition of more information such as crack distribution. Under the condition of using the data enhancement method, the mean absolute errors (MAEs) of the prediction results of the trained DNNs under the corresponding testing sets are 0.0191 and 0.0183, and the prediction results are in good agreement with the reference values. The image processing rates of the trained DNNs under the corresponding testing sets are all close to 75 images per second (resolution 512 × 512), which are 8.57 and 13.93 times as computationally efficient as the adopted deep learning-based multi-step crack quantification method.