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An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism

As a computing platform that can deal with problems independently and adapt to different environments, the brain-inspired function is similar to the human brain, which can effectively make use of visual targets and their surrounding background information to make more efficient and accurate decision...

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Autores principales: Shi, Hao, He, Cheng, Li, Jianhao, Chen, Liang, Wang, Yupei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748563/
https://www.ncbi.nlm.nih.gov/pubmed/36532272
http://dx.doi.org/10.3389/fnins.2022.1074706
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author Shi, Hao
He, Cheng
Li, Jianhao
Chen, Liang
Wang, Yupei
author_facet Shi, Hao
He, Cheng
Li, Jianhao
Chen, Liang
Wang, Yupei
author_sort Shi, Hao
collection PubMed
description As a computing platform that can deal with problems independently and adapt to different environments, the brain-inspired function is similar to the human brain, which can effectively make use of visual targets and their surrounding background information to make more efficient and accurate decision results. Currently synthetic aperture radar (SAR) ship target detection has an important role in military and civilian fields, but there are still great challenges in SAR ship target detection due to the problems of large span of ship scales and obvious feature differences. Therefore, this paper proposes an improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism, which efficiently focuses on target information ignoring the interference of complex background. First of all, most target detection algorithms are based on the anchor method, which requires a large number of anchors to be defined in advance and has poor generalization capability and performance to be improved in multi-scale ship detection, so this paper adopts an anchor-free detection network to directly enumerate potential target locations to enhance algorithm robustness and improve detection performance. Secondly, in order to improve the SAR ship target feature extraction capability, a dense connection module is proposed for the deep part of the network to promote more adequate deep feature fusion. A visual attention module is proposed for the shallow part of the network to focus on the salient features of the ship target in the local area for the input SAR images and suppress the interference of the surrounding background with similar scattering characteristics. In addition, because the SAR image coherent speckle noise is similar to the edge of the ship target, this paper proposes a novel width height prediction constraint to suppress the noise scattering power effect and improve the SAR ship localization accuracy. Moreover, to prove the effectiveness of this algorithm, experiments are conducted on the SAR ship detection dataset (SSDD) and high resolution SAR images dataset (HRSID). The experimental results show that the proposed algorithm achieves the best detection performance with metrics AP of 68.2% and 62.2% on SSDD and HRSID, respectively.
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spelling pubmed-97485632022-12-15 An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism Shi, Hao He, Cheng Li, Jianhao Chen, Liang Wang, Yupei Front Neurosci Neuroscience As a computing platform that can deal with problems independently and adapt to different environments, the brain-inspired function is similar to the human brain, which can effectively make use of visual targets and their surrounding background information to make more efficient and accurate decision results. Currently synthetic aperture radar (SAR) ship target detection has an important role in military and civilian fields, but there are still great challenges in SAR ship target detection due to the problems of large span of ship scales and obvious feature differences. Therefore, this paper proposes an improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism, which efficiently focuses on target information ignoring the interference of complex background. First of all, most target detection algorithms are based on the anchor method, which requires a large number of anchors to be defined in advance and has poor generalization capability and performance to be improved in multi-scale ship detection, so this paper adopts an anchor-free detection network to directly enumerate potential target locations to enhance algorithm robustness and improve detection performance. Secondly, in order to improve the SAR ship target feature extraction capability, a dense connection module is proposed for the deep part of the network to promote more adequate deep feature fusion. A visual attention module is proposed for the shallow part of the network to focus on the salient features of the ship target in the local area for the input SAR images and suppress the interference of the surrounding background with similar scattering characteristics. In addition, because the SAR image coherent speckle noise is similar to the edge of the ship target, this paper proposes a novel width height prediction constraint to suppress the noise scattering power effect and improve the SAR ship localization accuracy. Moreover, to prove the effectiveness of this algorithm, experiments are conducted on the SAR ship detection dataset (SSDD) and high resolution SAR images dataset (HRSID). The experimental results show that the proposed algorithm achieves the best detection performance with metrics AP of 68.2% and 62.2% on SSDD and HRSID, respectively. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9748563/ /pubmed/36532272 http://dx.doi.org/10.3389/fnins.2022.1074706 Text en Copyright © 2022 Shi, He, Li, Chen and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Shi, Hao
He, Cheng
Li, Jianhao
Chen, Liang
Wang, Yupei
An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism
title An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism
title_full An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism
title_fullStr An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism
title_full_unstemmed An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism
title_short An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism
title_sort improved anchor-free sar ship detection algorithm based on brain-inspired attention mechanism
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748563/
https://www.ncbi.nlm.nih.gov/pubmed/36532272
http://dx.doi.org/10.3389/fnins.2022.1074706
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