Using layer-wise training for Road Semantic Segmentation in Autonomous Cars

verfasst von: Shahrzad Shashaani, Mohammad Teshnehlab, Amirreza Khodadadian, Maryam Parvizi, Thomas Wick, Nima Noii
Abstract: A recently developed application of computer vision is pathfinding in self-driving cars. Semantic scene understanding and semantic segmentation, as subfields of computer vision, are widely used in autonomous driving. Semantic segmentation for pathfinding uses deep learning methods and various large sample datasets to train a proper model. Due to the importance of this task, accurate and robust models should be trained to perform properly in different lighting and weather conditions and in the presence of noisy input data. In this paper, we propose a novel learning method for semantic segmentation called layer-wise training and evaluate it on a light efficient structure called an efficient neural network (ENet). The results of the proposed learning method are compared with the classic learning approaches, including mIoU performance, network robustness to noise, and the possibility of reducing the size of the structure on two RGB image datasets on the road (CamVid) and off-road (Freiburg Forest) paths. Using this method partially eliminates the need for Transfer Learning. It also improves network performance when input is noisy.
Organisationseinheit(en): Institut für Angewandte Mathematik
Institut für Kontinuumsmechanik
Externe Organisation(en): K.N. Toosi University of Technology
Typ: Artikel
Journal: IEEE ACCESS
Band: 11
Seiten: 46320 - 46329
Anzahl der Seiten: 10
ISSN: 2169-3536
Publikationsdatum: 10.03.2023
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Ingenieurwesen (insg.), Werkstoffwissenschaften (insg.), Elektrotechnik und Elektronik, Informatik (insg.)
Elektronische Version(en): https://doi.org/10.1109/ACCESS.2023.3255988 (Zugang: Offen)
: Details im Forschungsportal „Research@Leibniz University“

BibTeX

@article{ffc7c85a44e14e9d90b86d13ee618ff7,
title = "Using layer-wise training for Road Semantic Segmentation in Autonomous Cars",
abstract = "A recently developed application of computer vision is pathfinding in self-driving cars. Semantic scene understanding and semantic segmentation, as subfields of computer vision, are widely used in autonomous driving. Semantic segmentation for pathfinding uses deep learning methods and various large sample datasets to train a proper model. Due to the importance of this task, accurate and robust models should be trained to perform properly in different lighting and weather conditions and in the presence of noisy input data. In this paper, we propose a novel learning method for semantic segmentation called layer-wise training and evaluate it on a light efficient structure called an efficient neural network (ENet). The results of the proposed learning method are compared with the classic learning approaches, including mIoU performance, network robustness to noise, and the possibility of reducing the size of the structure on two RGB image datasets on the road (CamVid) and off-road (Freiburg Forest) paths. Using this method partially eliminates the need for Transfer Learning. It also improves network performance when input is noisy.",
keywords = "Autonomous cars, Computer Vision, Convolution Neural Networks, Layer-wise trains, Semantic Segmentation, layer-wise trains, computer vision, convolution neural networks, semantic segmentation",
author = "Shahrzad Shashaani and Mohammad Teshnehlab and Amirreza Khodadadian and Maryam Parvizi and Thomas Wick and Nima Noii",
note = "Funding Information: The work of Maryam Parvizi was supported by the Alexander von Humboldt Foundation Project.",
year = "2023",
month = mar,
day = "10",
doi = "10.1109/ACCESS.2023.3255988",
language = "English",
volume = "11",
pages = "46320 -- 46329",
journal = "IEEE ACCESS",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}