Automated Semantic Segmentation for Autonomous Railway Vehicles

Abstract

With the development of computer vision methods, the number of areas where autonomous systems are used has also increased. Among these areas is the transportation sector. Autonomous systems in the transportation sector are mostly developed for road vehicles, but highway rules and standards different between countries. In this study, models capable of semantic segmentation have been developed for autonomous railway vehicles with the help of the public dataset. Four different U-Net models were trained with 8500 images for four different scenarios. The model trained for binary semantic segmentation reached mean Intersection over Union (mIoU) value of 89.1%, while the models trained for multi-class semantic segmentation reached 83.2% mIoU, 79.7% mIoU and 29.6% mIoU. Information about the inclusion of high-resolution images in model training and performance metrics in semantic segmentation studies shared.