Original scientific paper

https://doi.org/10.31803/tg-20220329114254

Automated Semantic Segmentation for Autonomous Railway Vehicles

Oğuzhan Katar orcid.org/0000-0002-5628-3543 ; Firat University, Department of Software Engineering, 23119 Elazig/Turkey
Erkan Duman orcid.org/0000-0003-2439-7244 ; Firat University, Department of Computer Engineering, 23119 Elazig/Turkey

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.

Keywords

autonomous systems; deep learning; railway vehicles; semantic segmentation; U-Net

Hrčak ID:

283783

URI

https://hrcak.srce.hr/283783

Publication date:

23.9.2022.

Visits: 1.181 *