This paper introduces a novel application of anomaly detection on the rail lines using deep learning methods on camera data. We propose a two-fold approach for identifying irregularities like coal, dirt, and obstacles on the rail tracks. In the first stage, a binary semantic segmentation is performed to extract only the rails from the background. In the second stage, we deploy our proposed autoencoder utilizing the self-supervised learning techniques to address the unavailability of labelled anomalies. The extracted rails from stage one are divided into multiple patches and are fed to the autoencoder, which is trained to reconstruct the non-anomalous data only. Hence, during the inference, the regeneration of images with any abnormalities produces a larger reconstruction error. Applying a predefined threshold to the reconstruction errors can detect an anomaly on a rail track. Stage one, rail extracting network achieves a high value of 52.78% mean Intersection over Union (mIoU). The second stage autoencoder network converges well on the training data. Finally, we evaluate our two-fold approach on real scenario test images, no false positives or false negatives were found in the the detected anomalies on the rail tracks.

Recent years have seen major advances in Artificial Intelligence (AI) methods for environment perception in intelligent transportation systems. Although most of them have been achieved in the automotive sector there is a similar demand in the railway domain. This paper investigates Deep Neural Network (DNN) based environment perception using vehicle-borne camera images from the rail domain. Specifically, railway switch detection and classification are addressed as a relevant example for a DNN application with potential use for landmark positioning, environment perception, and condition monitoring. The lack of large training data sets in the railway sector (in contrast to the automotive domain) is compensated by an appropriate DNN architecture, an anchor box ratio optimization scheme, and transfer learning. The presented experimental results advocate for the adopted approach.

Transportation mode detection from smartphone data is investigated as a relevant problem in the multi-modal transportation systems context. Neural networks are chosen as a timely and viable solution. The goal of this paper is to solve such a problem with a combination model of Convolutional Neural Network (CNN) and Bidirectional-Long short-term memory (BiLSTM) only processing accelerometer and magnetometer data. The performance in terms of accuracy and F1 score on the Sussex-Huawei Locomotion-Transportation (SHL) challenge 2018 dataset is comparable to methods that require the processing of a wider range of sensors. The uniqueness of our work is the light architecture requiring less computational resources for training and consequently a shorter inference time.