Intelligent road segmentation and obstacle detection for autonomous railway vehicle

Abstract

For autonomous railway vehicle with complex crisscrossed tracks, it is a huge challenge to intelligently detect the trespassers lying in the possible track regions where the train will move along. In order to solve the issue that the existing object detection algorithms detect all obstacles in traffic scene images, a novel strategy YOLOSEG is proposed for intelligent road segmentation and obstacle detection of railway trespasser. Unet is firstly trained to intelligently segment the railway track region where the train is likely to move on, and then the generated region mask is introduced into object detection network for recognizing obstacle within the mask area. The real video of the obstacle emerging in front of the train is difficult to record, therefore the traffic scenes taken from drivers’ perspectives are randomly combined with various obstacles to create the synthetic training dataset which covers various railway traffic scenarios and lighting conditions, and at the same time the label file is automatically generated. Furthermore, a random brightness strategy is proposed for dataset enhancement. By the performance evaluation comparison of FLOPs, Top-1 Accuracy, and mAP@0.5/%, abundant trespasser detection experiments based on synthetic dataset and real images verify the accuracy and effectiveness of the proposed method.