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A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms
To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575330/ https://www.ncbi.nlm.nih.gov/pubmed/37837012 http://dx.doi.org/10.3390/s23198182 |
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author | Zhu, Wenjie Li, Hongwei Cheng, Xianglong Jiang, Yirui |
author_facet | Zhu, Wenjie Li, Hongwei Cheng, Xianglong Jiang, Yirui |
author_sort | Zhu, Wenjie |
collection | PubMed |
description | To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processing and recommendation algorithms, the use of multi-task learning networks has been proven to reduce time, computing power, and storage usage in various task coupling cases. Due to the characteristics of the multi-task learning network, it has also been applied to visual road feature extraction in recent years. This article proposes a multi-task road feature extraction network that combines group convolution with transformer and squeeze excitation attention mechanisms. The network can simultaneously perform drivable area segmentation, lane line segmentation, and traffic object detection tasks. The experimental results of the BDD-100K dataset show that the proposed method performs well for different tasks and has a higher accuracy than similar algorithms. The proposed method provides new ideas and methods for the autonomous road perception of vehicles and the generation of highly accurate maps in visual-based autonomous driving processes. |
format | Online Article Text |
id | pubmed-10575330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105753302023-10-14 A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms Zhu, Wenjie Li, Hongwei Cheng, Xianglong Jiang, Yirui Sensors (Basel) Article To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processing and recommendation algorithms, the use of multi-task learning networks has been proven to reduce time, computing power, and storage usage in various task coupling cases. Due to the characteristics of the multi-task learning network, it has also been applied to visual road feature extraction in recent years. This article proposes a multi-task road feature extraction network that combines group convolution with transformer and squeeze excitation attention mechanisms. The network can simultaneously perform drivable area segmentation, lane line segmentation, and traffic object detection tasks. The experimental results of the BDD-100K dataset show that the proposed method performs well for different tasks and has a higher accuracy than similar algorithms. The proposed method provides new ideas and methods for the autonomous road perception of vehicles and the generation of highly accurate maps in visual-based autonomous driving processes. MDPI 2023-09-30 /pmc/articles/PMC10575330/ /pubmed/37837012 http://dx.doi.org/10.3390/s23198182 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Wenjie Li, Hongwei Cheng, Xianglong Jiang, Yirui A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms |
title | A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms |
title_full | A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms |
title_fullStr | A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms |
title_full_unstemmed | A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms |
title_short | A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms |
title_sort | multi-task road feature extraction network with grouped convolution and attention mechanisms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575330/ https://www.ncbi.nlm.nih.gov/pubmed/37837012 http://dx.doi.org/10.3390/s23198182 |
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