For sustainable operation and maintenance of urban railway infrastructure, intelligent visual inspection of the railway infrastructure attracts increasing attention to avoid unreliable, manual observation by humans at night, while trains do not operate. Although various automatic approaches were proposed using image processing and computer vision techniques, most of them are focused only on railway tracks. In this paper, we present a novel railway inspection system using facility detection based on deep convolutional neural network and computer vision-based image comparison approach. The proposed system aims to automatically detect wears and cracks by comparing a pair of corresponding image sets acquired at different times. We installed line scan camera on the roof of the train. Unlike an area-based camera, the line scan camera quickly acquires images with a wide field of view. The proposed system consists of three main modules: (i) image reconstruction for registration of facility positions, (ii) facility detection using an improved single shot detector, and (iii) deformed region detection using image processing and computer vision techniques. In experiments, we demonstrate that the proposed system accurately finds facilities and detects their potential defects. For that reason, the proposed system can provide various advantages such as cost reduction for maintenance and accident prevention.

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In this paper, we present an in-vehicle people localization technique using a deep neural network (DNN) model that is trained by the experimental data. First, an impulse radio ultra-wide band (IR-UWB) radar is installed inside the vehicle, and received signals are acquired by changing the arrangement of people sitting. Then, on the acquired data, we apply the DNN to train a classifier, which can predict whether a person is sitting or not in each seat. To design a network suitable for our system, we evaluate the performance by changing the type of activation function, the number of layers, and the number of nodes in each hidden layer of the DNN. In addition, we compare the performance of the proposed method with conventional machine learning algorithms such as support vector machine (SVM) and decision tree-based methods. From our measured signals, the proposed DNN-based method can classify all possible cases according to the location and number of people with an accuracy of 99 %. Moreover, the advantage of our proposed method is that there is no need to extract features from a given radar signal. INDEX TERMS Deep neural network, impulse radio ultra-wide band (IR-UWB) radar, people localization.