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
Autor*in: |
Ding, Jiangang [verfasserIn] Li, Wei [verfasserIn] Pei, Lili [verfasserIn] Yang, Ming [verfasserIn] Ye, Chao [verfasserIn] Yuan, Bo [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 217 |
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Übergeordnetes Werk: |
volume:217 |
DOI / URN: |
10.1016/j.eswa.2023.119560 |
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Katalog-ID: |
ELV062745379 |
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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 |
773 | 1 | 8 | |g volume:217 |
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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 |
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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 |
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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 |
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Sw-YoloX: An anchor-free detector based transformer for sea surface object detection |
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Sw-YoloX: An anchor-free detector based transformer for sea surface object detection |
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Ding, Jiangang Li, Wei Pei, Lili Yang, Ming Ye, Chao Yuan, Bo |
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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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Sw-YoloX: An anchor-free detector based transformer for sea surface object detection |
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