Camouflaged object detection based on context-aware and boundary refinement

Abstract Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, le...
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Autor*in:	Shi, Caijuan [verfasserIn] Ren, Bijuan Chen, Houru Zhao, Lin Lin, Chunyu Zhao, Yao

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Camouflaged object detection Context information Boundary refinement Attention mechanism

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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.

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2023), 19 vom: 28. Juni, Seite 22429-22445
Übergeordnetes Werk:	volume:53 ; year:2023 ; number:19 ; day:28 ; month:06 ; pages:22429-22445

Links:	Volltext

DOI / URN:	10.1007/s10489-023-04645-x

Katalog-ID:	SPR053444809

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520			\|a Abstract Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics.
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allfields	10.1007/s10489-023-04645-x doi (DE-627)SPR053444809 (SPR)s10489-023-04645-x-e DE-627 ger DE-627 rakwb eng Shi, Caijuan verfasserin aut Camouflaged object detection based on context-aware and boundary refinement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. Camouflaged object detection (dpeaa)DE-He213 Context information (dpeaa)DE-He213 Boundary refinement (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Ren, Bijuan aut Chen, Houru aut Zhao, Lin aut Lin, Chunyu aut Zhao, Yao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 19 vom: 28. Juni, Seite 22429-22445 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:19 day:28 month:06 pages:22429-22445 https://dx.doi.org/10.1007/s10489-023-04645-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 53 2023 19 28 06 22429-22445
spelling	10.1007/s10489-023-04645-x doi (DE-627)SPR053444809 (SPR)s10489-023-04645-x-e DE-627 ger DE-627 rakwb eng Shi, Caijuan verfasserin aut Camouflaged object detection based on context-aware and boundary refinement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. Camouflaged object detection (dpeaa)DE-He213 Context information (dpeaa)DE-He213 Boundary refinement (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Ren, Bijuan aut Chen, Houru aut Zhao, Lin aut Lin, Chunyu aut Zhao, Yao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 19 vom: 28. Juni, Seite 22429-22445 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:19 day:28 month:06 pages:22429-22445 https://dx.doi.org/10.1007/s10489-023-04645-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 53 2023 19 28 06 22429-22445
allfields_unstemmed	10.1007/s10489-023-04645-x doi (DE-627)SPR053444809 (SPR)s10489-023-04645-x-e DE-627 ger DE-627 rakwb eng Shi, Caijuan verfasserin aut Camouflaged object detection based on context-aware and boundary refinement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. Camouflaged object detection (dpeaa)DE-He213 Context information (dpeaa)DE-He213 Boundary refinement (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Ren, Bijuan aut Chen, Houru aut Zhao, Lin aut Lin, Chunyu aut Zhao, Yao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 19 vom: 28. Juni, Seite 22429-22445 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:19 day:28 month:06 pages:22429-22445 https://dx.doi.org/10.1007/s10489-023-04645-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 53 2023 19 28 06 22429-22445
allfieldsGer	10.1007/s10489-023-04645-x doi (DE-627)SPR053444809 (SPR)s10489-023-04645-x-e DE-627 ger DE-627 rakwb eng Shi, Caijuan verfasserin aut Camouflaged object detection based on context-aware and boundary refinement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. Camouflaged object detection (dpeaa)DE-He213 Context information (dpeaa)DE-He213 Boundary refinement (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Ren, Bijuan aut Chen, Houru aut Zhao, Lin aut Lin, Chunyu aut Zhao, Yao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 19 vom: 28. Juni, Seite 22429-22445 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:19 day:28 month:06 pages:22429-22445 https://dx.doi.org/10.1007/s10489-023-04645-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 53 2023 19 28 06 22429-22445
allfieldsSound	10.1007/s10489-023-04645-x doi (DE-627)SPR053444809 (SPR)s10489-023-04645-x-e DE-627 ger DE-627 rakwb eng Shi, Caijuan verfasserin aut Camouflaged object detection based on context-aware and boundary refinement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. Camouflaged object detection (dpeaa)DE-He213 Context information (dpeaa)DE-He213 Boundary refinement (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Ren, Bijuan aut Chen, Houru aut Zhao, Lin aut Lin, Chunyu aut Zhao, Yao aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 19 vom: 28. Juni, Seite 22429-22445 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:19 day:28 month:06 pages:22429-22445 https://dx.doi.org/10.1007/s10489-023-04645-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 53 2023 19 28 06 22429-22445
language	English
source	Enthalten in Applied intelligence 53(2023), 19 vom: 28. Juni, Seite 22429-22445 volume:53 year:2023 number:19 day:28 month:06 pages:22429-22445
sourceStr	Enthalten in Applied intelligence 53(2023), 19 vom: 28. Juni, Seite 22429-22445 volume:53 year:2023 number:19 day:28 month:06 pages:22429-22445
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Camouflaged object detection Context information Boundary refinement Attention mechanism
isfreeaccess_bool	false
container_title	Applied intelligence
authorswithroles_txt_mv	Shi, Caijuan @@aut@@ Ren, Bijuan @@aut@@ Chen, Houru @@aut@@ Zhao, Lin @@aut@@ Lin, Chunyu @@aut@@ Zhao, Yao @@aut@@
publishDateDaySort_date	2023-06-28T00:00:00Z
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author	Shi, Caijuan
spellingShingle	Shi, Caijuan misc Camouflaged object detection misc Context information misc Boundary refinement misc Attention mechanism Camouflaged object detection based on context-aware and boundary refinement
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topic_title	Camouflaged object detection based on context-aware and boundary refinement Camouflaged object detection (dpeaa)DE-He213 Context information (dpeaa)DE-He213 Boundary refinement (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213
topic	misc Camouflaged object detection misc Context information misc Boundary refinement misc Attention mechanism
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title	Camouflaged object detection based on context-aware and boundary refinement
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title_full	Camouflaged object detection based on context-aware and boundary refinement
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author_browse	Shi, Caijuan Ren, Bijuan Chen, Houru Zhao, Lin Lin, Chunyu Zhao, Yao
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title_sort	camouflaged object detection based on context-aware and boundary refinement
title_auth	Camouflaged object detection based on context-aware and boundary refinement
abstract	Abstract Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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.
abstractGer	Abstract Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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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title_short	Camouflaged object detection based on context-aware and boundary refinement
url	https://dx.doi.org/10.1007/s10489-023-04645-x
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author2	Ren, Bijuan Chen, Houru Zhao, Lin Lin, Chunyu Zhao, Yao
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doi_str	10.1007/s10489-023-04645-x
up_date	2024-07-03T19:34:56.032Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053444809</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231019064639.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231019s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-023-04645-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053444809</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10489-023-04645-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Shi, Caijuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Camouflaged object detection based on context-aware and boundary refinement</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. 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