Sw-YoloX: An anchor-free detector based transformer for sea surface object detection

To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be ca...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ding, Jiangang [verfasserIn] Li, Wei [verfasserIn] Pei, Lili [verfasserIn] Yang, Ming [verfasserIn] Ye, Chao [verfasserIn] Yuan, Bo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 217
Übergeordnetes Werk:	volume:217

DOI / URN:	10.1016/j.eswa.2023.119560

Katalog-ID:	ELV062745379

Internformat


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520			\|a To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling.
650		4	\|a Sea surface object detection
650		4	\|a Sw-YoloX
650		4	\|a Transformer
650		4	\|a YoloX
650		4	\|a Self-training classifier
700	1		\|a Li, Wei \|e verfasserin \|0 (orcid)0000-0003-4508-3076 \|4 aut
700	1		\|a Pei, Lili \|e verfasserin \|4 aut
700	1		\|a Yang, Ming \|e verfasserin \|4 aut
700	1		\|a Ye, Chao \|e verfasserin \|4 aut
700	1		\|a Yuan, Bo \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 217 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
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912			\|a GBV_ILN_32
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912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
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912			\|a GBV_ILN_70
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912			\|a GBV_ILN_95
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912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
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912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
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912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
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912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
952			\|d 217

Indexfelder

author_variant	j d jd w l wl l p lp m y my c y cy b y by
matchkey_str	dingjiangangliweipeililiyangmingyechaoyu:2023----:wooaacofedtcobsdrnfrefres
hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.eswa.2023.119560 doi (DE-627)ELV062745379 (ELSEVIER)S0957-4174(23)00061-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Ding, Jiangang verfasserin (orcid)0000-0002-8751-966X aut Sw-YoloX: An anchor-free detector based transformer for sea surface object detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling. Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier Li, Wei verfasserin (orcid)0000-0003-4508-3076 aut Pei, Lili verfasserin aut Yang, Ming verfasserin aut Ye, Chao verfasserin aut Yuan, Bo verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
spelling	10.1016/j.eswa.2023.119560 doi (DE-627)ELV062745379 (ELSEVIER)S0957-4174(23)00061-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Ding, Jiangang verfasserin (orcid)0000-0002-8751-966X aut Sw-YoloX: An anchor-free detector based transformer for sea surface object detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling. Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier Li, Wei verfasserin (orcid)0000-0003-4508-3076 aut Pei, Lili verfasserin aut Yang, Ming verfasserin aut Ye, Chao verfasserin aut Yuan, Bo verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
allfields_unstemmed	10.1016/j.eswa.2023.119560 doi (DE-627)ELV062745379 (ELSEVIER)S0957-4174(23)00061-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Ding, Jiangang verfasserin (orcid)0000-0002-8751-966X aut Sw-YoloX: An anchor-free detector based transformer for sea surface object detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling. Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier Li, Wei verfasserin (orcid)0000-0003-4508-3076 aut Pei, Lili verfasserin aut Yang, Ming verfasserin aut Ye, Chao verfasserin aut Yuan, Bo verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
allfieldsGer	10.1016/j.eswa.2023.119560 doi (DE-627)ELV062745379 (ELSEVIER)S0957-4174(23)00061-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Ding, Jiangang verfasserin (orcid)0000-0002-8751-966X aut Sw-YoloX: An anchor-free detector based transformer for sea surface object detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling. Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier Li, Wei verfasserin (orcid)0000-0003-4508-3076 aut Pei, Lili verfasserin aut Yang, Ming verfasserin aut Ye, Chao verfasserin aut Yuan, Bo verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
allfieldsSound	10.1016/j.eswa.2023.119560 doi (DE-627)ELV062745379 (ELSEVIER)S0957-4174(23)00061-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Ding, Jiangang verfasserin (orcid)0000-0002-8751-966X aut Sw-YoloX: An anchor-free detector based transformer for sea surface object detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling. Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier Li, Wei verfasserin (orcid)0000-0003-4508-3076 aut Pei, Lili verfasserin aut Yang, Ming verfasserin aut Ye, Chao verfasserin aut Yuan, Bo verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 217 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:217 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 217
language	English
source	Enthalten in Expert systems with applications 217 volume:217
sourceStr	Enthalten in Expert systems with applications 217 volume:217
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Ding, Jiangang @@aut@@ Li, Wei @@aut@@ Pei, Lili @@aut@@ Yang, Ming @@aut@@ Ye, Chao @@aut@@ Yuan, Bo @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV062745379
language_de	englisch
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author	Ding, Jiangang
spellingShingle	Ding, Jiangang ddc 004 bkl 54.72 misc Sea surface object detection misc Sw-YoloX misc Transformer misc YoloX misc Self-training classifier Sw-YoloX: An anchor-free detector based transformer for sea surface object detection
authorStr	Ding, Jiangang
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topic_title	004 VZ 54.72 bkl Sw-YoloX: An anchor-free detector based transformer for sea surface object detection Sea surface object detection Sw-YoloX Transformer YoloX Self-training classifier
topic	ddc 004 bkl 54.72 misc Sea surface object detection misc Sw-YoloX misc Transformer misc YoloX misc Self-training classifier
topic_unstemmed	ddc 004 bkl 54.72 misc Sea surface object detection misc Sw-YoloX misc Transformer misc YoloX misc Self-training classifier
topic_browse	ddc 004 bkl 54.72 misc Sea surface object detection misc Sw-YoloX misc Transformer misc YoloX misc Self-training classifier
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title	Sw-YoloX: An anchor-free detector based transformer for sea surface object detection
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title_full	Sw-YoloX: An anchor-free detector based transformer for sea surface object detection
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author_browse	Ding, Jiangang Li, Wei Pei, Lili Yang, Ming Ye, Chao Yuan, Bo
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author-letter	Ding, Jiangang
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title_sort	sw-yolox: an anchor-free detector based transformer for sea surface object detection
title_auth	Sw-YoloX: An anchor-free detector based transformer for sea surface object detection
abstract	To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling.
abstractGer	To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling.
abstract_unstemmed	To cope with the challenge of blurred images of sea surface objects caused by the complex and undulating sea surface environment, we propose Sw-YoloX, which can utilize the global modeling ability to encode the key semantics of sea surface objects, thereby obtaining global features that cannot be captured by CNN. Then the convolutional block attention module (CBAM) and atrous spatial pyramid pooling (ASPP) module are integrated in the neck of the detector, and the decoupled head is used as the prediction part. In addition, we also integrate multiple training strategies to effectively improve the detector performance, such as simple optimal transport assignment (SimOTA) strategy and multi-model integration. Finally, we construct the XM-10000 dataset for validation based on sea surface monitoring data in Xiamen, China. With end-to-end training, Sw-YoloX achieves higher performance than baseline and mainstream detector, with F1-Score is 78.1, mean average precision (mAP) is 54.4, and average recall (AR) is 72.0. This research, which has now been deployed in the coastal defense department in Xiamen, China, has important implications for searching for survivors and preventing smuggling.
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title_short	Sw-YoloX: An anchor-free detector based transformer for sea surface object detection
remote_bool	true
author2	Li, Wei Pei, Lili Yang, Ming Ye, Chao Yuan, Bo
author2Str	Li, Wei Pei, Lili Yang, Ming Ye, Chao Yuan, Bo
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doi_str	10.1016/j.eswa.2023.119560
up_date	2024-07-06T19:08:04.332Z
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Sw-YoloX: An anchor-free detector based transformer for sea surface object detection

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