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Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism
Semantic segmentation and depth estimation are crucial components in the field of autonomous driving for scene understanding. Jointly learning these tasks can lead to a better understanding of scenarios. However, using task-specific networks to extract global features from task-shared networks can b...
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/PMC10490601/ https://www.ncbi.nlm.nih.gov/pubmed/37687922 http://dx.doi.org/10.3390/s23177466 |
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author | Ji, Naihua Dong, Huiqian Meng, Fanyun Pang, Liping |
author_facet | Ji, Naihua Dong, Huiqian Meng, Fanyun Pang, Liping |
author_sort | Ji, Naihua |
collection | PubMed |
description | Semantic segmentation and depth estimation are crucial components in the field of autonomous driving for scene understanding. Jointly learning these tasks can lead to a better understanding of scenarios. However, using task-specific networks to extract global features from task-shared networks can be inadequate. To address this issue, we propose a multi-task residual attention network (MTRAN) that consists of a global shared network and two attention networks dedicated to semantic segmentation and depth estimation. The convolutional block attention module is used to highlight the global feature map, and residual connections are added to prevent network degradation problems. To ensure manageable task loss and prevent specific tasks from dominating the training process, we introduce a random-weighted strategy into the impartial multi-task learning method. We conduct experiments to demonstrate the effectiveness of the proposed method. |
format | Online Article Text |
id | pubmed-10490601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104906012023-09-09 Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism Ji, Naihua Dong, Huiqian Meng, Fanyun Pang, Liping Sensors (Basel) Article Semantic segmentation and depth estimation are crucial components in the field of autonomous driving for scene understanding. Jointly learning these tasks can lead to a better understanding of scenarios. However, using task-specific networks to extract global features from task-shared networks can be inadequate. To address this issue, we propose a multi-task residual attention network (MTRAN) that consists of a global shared network and two attention networks dedicated to semantic segmentation and depth estimation. The convolutional block attention module is used to highlight the global feature map, and residual connections are added to prevent network degradation problems. To ensure manageable task loss and prevent specific tasks from dominating the training process, we introduce a random-weighted strategy into the impartial multi-task learning method. We conduct experiments to demonstrate the effectiveness of the proposed method. MDPI 2023-08-28 /pmc/articles/PMC10490601/ /pubmed/37687922 http://dx.doi.org/10.3390/s23177466 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 Ji, Naihua Dong, Huiqian Meng, Fanyun Pang, Liping Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism |
title | Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism |
title_full | Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism |
title_fullStr | Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism |
title_full_unstemmed | Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism |
title_short | Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism |
title_sort | semantic segmentation and depth estimation based on residual attention mechanism |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490601/ https://www.ncbi.nlm.nih.gov/pubmed/37687922 http://dx.doi.org/10.3390/s23177466 |
work_keys_str_mv | AT jinaihua semanticsegmentationanddepthestimationbasedonresidualattentionmechanism AT donghuiqian semanticsegmentationanddepthestimationbasedonresidualattentionmechanism AT mengfanyun semanticsegmentationanddepthestimationbasedonresidualattentionmechanism AT pangliping semanticsegmentationanddepthestimationbasedonresidualattentionmechanism |