Fast camouflaged object detection via multi-scale feature-enhanced network
Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ER...
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| Autor*in: |
Zhou, Bingqin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Signal, image and video processing - Springer London, 2007, 18(2024), 4 vom: 28. Feb., Seite 3903-3914 |
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| Übergeordnetes Werk: |
volume:18 ; year:2024 ; number:4 ; day:28 ; month:02 ; pages:3903-3914 |
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| DOI / URN: |
10.1007/s11760-024-03051-1 |
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| Katalog-ID: |
SPR055291643 |
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| 520 | |a Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). | ||
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| 650 | 4 | |a Multi-scale feature enhancement |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Convolutional neural network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fast detection |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yang, Kun |4 aut | |
| 700 | 1 | |a Gao, Zhigang |4 aut | |
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10.1007/s11760-024-03051-1 doi (DE-627)SPR055291643 (SPR)s11760-024-03051-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Zhou, Bingqin verfasserin aut Fast camouflaged object detection via multi-scale feature-enhanced network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). Camouflaged object detection (dpeaa)DE-He213 Multi-scale feature enhancement (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Fast detection (dpeaa)DE-He213 Yang, Kun aut Gao, Zhigang aut Enthalten in Signal, image and video processing Springer London, 2007 18(2024), 4 vom: 28. Feb., Seite 3903-3914 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2024 number:4 day:28 month:02 pages:3903-3914 https://dx.doi.org/10.1007/s11760-024-03051-1 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 4 28 02 3903-3914 |
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10.1007/s11760-024-03051-1 doi (DE-627)SPR055291643 (SPR)s11760-024-03051-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Zhou, Bingqin verfasserin aut Fast camouflaged object detection via multi-scale feature-enhanced network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). Camouflaged object detection (dpeaa)DE-He213 Multi-scale feature enhancement (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Fast detection (dpeaa)DE-He213 Yang, Kun aut Gao, Zhigang aut Enthalten in Signal, image and video processing Springer London, 2007 18(2024), 4 vom: 28. Feb., Seite 3903-3914 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2024 number:4 day:28 month:02 pages:3903-3914 https://dx.doi.org/10.1007/s11760-024-03051-1 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 4 28 02 3903-3914 |
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10.1007/s11760-024-03051-1 doi (DE-627)SPR055291643 (SPR)s11760-024-03051-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Zhou, Bingqin verfasserin aut Fast camouflaged object detection via multi-scale feature-enhanced network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). Camouflaged object detection (dpeaa)DE-He213 Multi-scale feature enhancement (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Fast detection (dpeaa)DE-He213 Yang, Kun aut Gao, Zhigang aut Enthalten in Signal, image and video processing Springer London, 2007 18(2024), 4 vom: 28. Feb., Seite 3903-3914 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2024 number:4 day:28 month:02 pages:3903-3914 https://dx.doi.org/10.1007/s11760-024-03051-1 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 4 28 02 3903-3914 |
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10.1007/s11760-024-03051-1 doi (DE-627)SPR055291643 (SPR)s11760-024-03051-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Zhou, Bingqin verfasserin aut Fast camouflaged object detection via multi-scale feature-enhanced network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). Camouflaged object detection (dpeaa)DE-He213 Multi-scale feature enhancement (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Fast detection (dpeaa)DE-He213 Yang, Kun aut Gao, Zhigang aut Enthalten in Signal, image and video processing Springer London, 2007 18(2024), 4 vom: 28. Feb., Seite 3903-3914 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2024 number:4 day:28 month:02 pages:3903-3914 https://dx.doi.org/10.1007/s11760-024-03051-1 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 4 28 02 3903-3914 |
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10.1007/s11760-024-03051-1 doi (DE-627)SPR055291643 (SPR)s11760-024-03051-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Zhou, Bingqin verfasserin aut Fast camouflaged object detection via multi-scale feature-enhanced network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). Camouflaged object detection (dpeaa)DE-He213 Multi-scale feature enhancement (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Fast detection (dpeaa)DE-He213 Yang, Kun aut Gao, Zhigang aut Enthalten in Signal, image and video processing Springer London, 2007 18(2024), 4 vom: 28. Feb., Seite 3903-3914 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2024 number:4 day:28 month:02 pages:3903-3914 https://dx.doi.org/10.1007/s11760-024-03051-1 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 4 28 02 3903-3914 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. 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Fast camouflaged object detection via multi-scale feature-enhanced network |
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Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The aim of camouflaged object detection (COD) is to identify objects that are hidden or camouflaged in the visual scene. Since camouflaged objects have fuzzy boundaries and are very similar to their surroundings, the task of COD, especially multi-scale COD, is still challenging. Based on ERRNet, we proposed a multi-scale feature-enhanced network (MSFENet). Specifically, we have developed a multi-scale feature enhancement module (MFEM), which adopts a coarse-to-fine manner to improve the ability of a single layer to represent multi-scale information. This module can extract more complete large-scale target feature information and retain much more small-scale target feature information and less regional background information. The experimental results on publicly available datasets show that our proposed MSFENet outperforms 10 mainstream methods. The ablation studies show that the proposed module is effective in improving the detection performance of multi-scale camouflaging objects and improving the overall performance. Compared with ERRNet, the average $$S_\alpha $$, $$E_\phi $$ and $$F_\beta ^W $$ scores of the MSFENet are 1.2%, 0.6% and 2.5% higher for the multi-scale COD task. In addition, the proposed MSFENet can be directly used for real-time detection due to its fast inference capability (i.e., 75.3 frames per second). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Fast camouflaged object detection via multi-scale feature-enhanced network |
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https://dx.doi.org/10.1007/s11760-024-03051-1 |
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Yang, Kun Gao, Zhigang |
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10.1007/s11760-024-03051-1 |
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2024-07-03T14:39:14.022Z |
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| score |
7.401269 |