SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images

Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they g...
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Autor*in:	Ping Han [verfasserIn] Dayu Liao [verfasserIn] Binbin Han [verfasserIn] Zheng Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 18, p 4669
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:18, p 4669

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14184669

Katalog-ID:	DOAJ033897875

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allfields	10.3390/rs14184669 doi (DE-627)DOAJ033897875 (DE-599)DOAJ23beaba2aceb41928e0d413ddf938c56 DE-627 ger DE-627 rakwb eng Ping Han verfasserin aut SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs. synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization Science Q Dayu Liao verfasserin aut Binbin Han verfasserin aut Zheng Cheng verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 18, p 4669 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:18, p 4669 https://doi.org/10.3390/rs14184669 kostenfrei https://doaj.org/article/23beaba2aceb41928e0d413ddf938c56 kostenfrei https://www.mdpi.com/2072-4292/14/18/4669 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 18, p 4669
spelling	10.3390/rs14184669 doi (DE-627)DOAJ033897875 (DE-599)DOAJ23beaba2aceb41928e0d413ddf938c56 DE-627 ger DE-627 rakwb eng Ping Han verfasserin aut SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs. synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization Science Q Dayu Liao verfasserin aut Binbin Han verfasserin aut Zheng Cheng verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 18, p 4669 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:18, p 4669 https://doi.org/10.3390/rs14184669 kostenfrei https://doaj.org/article/23beaba2aceb41928e0d413ddf938c56 kostenfrei https://www.mdpi.com/2072-4292/14/18/4669 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 18, p 4669
allfields_unstemmed	10.3390/rs14184669 doi (DE-627)DOAJ033897875 (DE-599)DOAJ23beaba2aceb41928e0d413ddf938c56 DE-627 ger DE-627 rakwb eng Ping Han verfasserin aut SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs. synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization Science Q Dayu Liao verfasserin aut Binbin Han verfasserin aut Zheng Cheng verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 18, p 4669 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:18, p 4669 https://doi.org/10.3390/rs14184669 kostenfrei https://doaj.org/article/23beaba2aceb41928e0d413ddf938c56 kostenfrei https://www.mdpi.com/2072-4292/14/18/4669 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 18, p 4669
allfieldsGer	10.3390/rs14184669 doi (DE-627)DOAJ033897875 (DE-599)DOAJ23beaba2aceb41928e0d413ddf938c56 DE-627 ger DE-627 rakwb eng Ping Han verfasserin aut SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs. synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization Science Q Dayu Liao verfasserin aut Binbin Han verfasserin aut Zheng Cheng verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 18, p 4669 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:18, p 4669 https://doi.org/10.3390/rs14184669 kostenfrei https://doaj.org/article/23beaba2aceb41928e0d413ddf938c56 kostenfrei https://www.mdpi.com/2072-4292/14/18/4669 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 18, p 4669
allfieldsSound	10.3390/rs14184669 doi (DE-627)DOAJ033897875 (DE-599)DOAJ23beaba2aceb41928e0d413ddf938c56 DE-627 ger DE-627 rakwb eng Ping Han verfasserin aut SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs. synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization Science Q Dayu Liao verfasserin aut Binbin Han verfasserin aut Zheng Cheng verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 18, p 4669 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:18, p 4669 https://doi.org/10.3390/rs14184669 kostenfrei https://doaj.org/article/23beaba2aceb41928e0d413ddf938c56 kostenfrei https://www.mdpi.com/2072-4292/14/18/4669 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 18, p 4669
language	English
source	In Remote Sensing 14(2022), 18, p 4669 volume:14 year:2022 number:18, p 4669
sourceStr	In Remote Sensing 14(2022), 18, p 4669 volume:14 year:2022 number:18, p 4669
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Ping Han @@aut@@ Dayu Liao @@aut@@ Binbin Han @@aut@@ Zheng Cheng @@aut@@
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author	Ping Han
spellingShingle	Ping Han misc synthetic aperture radar (SAR) misc aircraft detection misc attention mechanism misc residual dilated misc structural re-parameterization misc Science misc Q SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images
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topic_title	SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images synthetic aperture radar (SAR) aircraft detection attention mechanism residual dilated structural re-parameterization
topic	misc synthetic aperture radar (SAR) misc aircraft detection misc attention mechanism misc residual dilated misc structural re-parameterization misc Science misc Q
topic_unstemmed	misc synthetic aperture radar (SAR) misc aircraft detection misc attention mechanism misc residual dilated misc structural re-parameterization misc Science misc Q
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title	SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images
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title_full	SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images
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title_sort	sean: a simple and efficient attention network for aircraft detection in sar images
title_auth	SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images
abstract	Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs.
abstractGer	Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs.
abstract_unstemmed	Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs.
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title_short	SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images
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