@article{ref1,
title="Unification of road scene segmentation strategies using multistream data and latent space attention",
journal="Sensors (Basel)",
year="2023",
author="Naudé, August J. and Myburgh, Herman C.",
volume="23",
number="17",
pages="e7355-e7355",
abstract="Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demonstrated how deep learning methods can be combined to improve the segmentation and perception performance of road scene understanding systems. This paper proposes a novel segmentation system that uses fully connected networks, attention mechanisms, and multiple-input data stream fusion to improve segmentation performance. <br><br>RESULTS show comparable performance compared to previous works, with a mean intersection over union of 87.4% on the Cityscapes dataset.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23177355",
url="http://dx.doi.org/10.3390/s23177355"
}