Segmentation of Track Surface Defects Based on Machine Vision and Neural Networks

Local failure of rail track commonly grow from surface defects. Hence, timely detection of surface defects helps to identify potential hazards on the track and reduce the occurrence of railroad accidents. Since track surface defects are scattered and diverse, and different service life leads to different types of defects, it is crucial to detect surface defects in real time, with efficiency, reliability and robustness. To this end, a pixel-level defects segmentation method is proposed. In this paper, features are tessellated together at the Channel level to form denser features, allowing additional information on surface defects textures to be propagated among high-resolution layers. Dropout is performed on the weak correlations learned during the convolution, so that the convolution blocks can share a uniform weight matrix, reducing computation redundancy and model complexity. Firstly, the track datasets of different ages are categorized into four sets, then, the samples are normalized to grey scale by pre-processing, and fed into the proposed network for training. An evaluation of the proposed model on defective samples was performed to demonstrate the performance of the method, with an Accuracy of 97.47%, a Loss of 0.0061 and an Average Frame Rate of 0.033s. The results of different networks tested on the same dataset show that the proposed model exhibits strong stability, adaptability and robustness. In addition, the proposed model is assessed on two different datasets with distinct challenges, with Mean Intersection over Union yielding 2.13% and 3.77% boosts respectively.