High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment
Abstract Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting compu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Su, Shuzhi [verfasserIn] Tang, Zefang [verfasserIn] Zhu, Yanmin [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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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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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| Übergeordnetes Werk: |
Enthalten in: The visual computer - Springer Berlin Heidelberg, 1985, 40(2023), 6 vom: 05. Okt., Seite 4355-4371 |
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| Übergeordnetes Werk: |
volume:40 ; year:2023 ; number:6 ; day:05 ; month:10 ; pages:4355-4371 |
| Links: |
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| DOI / URN: |
10.1007/s00371-023-03086-4 |
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| Katalog-ID: |
SPR056137931 |
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| 520 | |a Abstract Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. | ||
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10.1007/s00371-023-03086-4 doi (DE-627)SPR056137931 (SPR)s00371-023-03086-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.73 bkl Su, Shuzhi verfasserin aut High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. Object detection (dpeaa)DE-He213 Bounding box loss functions (dpeaa)DE-He213 Convolutional modules (dpeaa)DE-He213 Small objects (dpeaa)DE-He213 Auxiliary-point balancing IoU (dpeaa)DE-He213 Tang, Zefang verfasserin aut Zhu, Yanmin verfasserin aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 40(2023), 6 vom: 05. Okt., Seite 4355-4371 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:6 day:05 month:10 pages:4355-4371 https://dx.doi.org/10.1007/s00371-023-03086-4 X:SPRINGER Resolving-System 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_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_267 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 54.73 VZ AR 40 2023 6 05 10 4355-4371 |
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10.1007/s00371-023-03086-4 doi (DE-627)SPR056137931 (SPR)s00371-023-03086-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.73 bkl Su, Shuzhi verfasserin aut High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. Object detection (dpeaa)DE-He213 Bounding box loss functions (dpeaa)DE-He213 Convolutional modules (dpeaa)DE-He213 Small objects (dpeaa)DE-He213 Auxiliary-point balancing IoU (dpeaa)DE-He213 Tang, Zefang verfasserin aut Zhu, Yanmin verfasserin aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 40(2023), 6 vom: 05. Okt., Seite 4355-4371 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:6 day:05 month:10 pages:4355-4371 https://dx.doi.org/10.1007/s00371-023-03086-4 X:SPRINGER Resolving-System 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_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_267 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 54.73 VZ AR 40 2023 6 05 10 4355-4371 |
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10.1007/s00371-023-03086-4 doi (DE-627)SPR056137931 (SPR)s00371-023-03086-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.73 bkl Su, Shuzhi verfasserin aut High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. Object detection (dpeaa)DE-He213 Bounding box loss functions (dpeaa)DE-He213 Convolutional modules (dpeaa)DE-He213 Small objects (dpeaa)DE-He213 Auxiliary-point balancing IoU (dpeaa)DE-He213 Tang, Zefang verfasserin aut Zhu, Yanmin verfasserin aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 40(2023), 6 vom: 05. Okt., Seite 4355-4371 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:6 day:05 month:10 pages:4355-4371 https://dx.doi.org/10.1007/s00371-023-03086-4 X:SPRINGER Resolving-System 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_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_267 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 54.73 VZ AR 40 2023 6 05 10 4355-4371 |
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10.1007/s00371-023-03086-4 doi (DE-627)SPR056137931 (SPR)s00371-023-03086-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.73 bkl Su, Shuzhi verfasserin aut High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. Object detection (dpeaa)DE-He213 Bounding box loss functions (dpeaa)DE-He213 Convolutional modules (dpeaa)DE-He213 Small objects (dpeaa)DE-He213 Auxiliary-point balancing IoU (dpeaa)DE-He213 Tang, Zefang verfasserin aut Zhu, Yanmin verfasserin aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 40(2023), 6 vom: 05. Okt., Seite 4355-4371 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:6 day:05 month:10 pages:4355-4371 https://dx.doi.org/10.1007/s00371-023-03086-4 X:SPRINGER Resolving-System 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_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_267 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 54.73 VZ AR 40 2023 6 05 10 4355-4371 |
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10.1007/s00371-023-03086-4 doi (DE-627)SPR056137931 (SPR)s00371-023-03086-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.73 bkl Su, Shuzhi verfasserin aut High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. Object detection (dpeaa)DE-He213 Bounding box loss functions (dpeaa)DE-He213 Convolutional modules (dpeaa)DE-He213 Small objects (dpeaa)DE-He213 Auxiliary-point balancing IoU (dpeaa)DE-He213 Tang, Zefang verfasserin aut Zhu, Yanmin verfasserin aut Enthalten in The visual computer Springer Berlin Heidelberg, 1985 40(2023), 6 vom: 05. Okt., Seite 4355-4371 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:6 day:05 month:10 pages:4355-4371 https://dx.doi.org/10.1007/s00371-023-03086-4 X:SPRINGER Resolving-System 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_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_267 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 54.73 VZ AR 40 2023 6 05 10 4355-4371 |
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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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Object detection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bounding box loss functions</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convolutional modules</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Small objects</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Auxiliary-point balancing IoU</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tang, Zefang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Yanmin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The visual computer</subfield><subfield code="d">Springer Berlin Heidelberg, 1985</subfield><subfield code="g">40(2023), 6 vom: 05. 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Su, Shuzhi |
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Su, Shuzhi ddc 004 bkl 54.73 misc Object detection misc Bounding box loss functions misc Convolutional modules misc Small objects misc Auxiliary-point balancing IoU High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment |
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004 VZ 54.73 bkl High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment Object detection (dpeaa)DE-He213 Bounding box loss functions (dpeaa)DE-He213 Convolutional modules (dpeaa)DE-He213 Small objects (dpeaa)DE-He213 Auxiliary-point balancing IoU (dpeaa)DE-He213 |
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high-density foreground object detection in optical remote sensing images via semantic fusion and box alignment |
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High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment |
| abstract |
Abstract Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Accuracy and effectiveness towards multiscale and dense remote sensing multivariate 2D information with object detection of bi-directional learning method remains challenging. Most methods require the design of complex network structures or bounding box loss functions, thus neglecting computational cost and training noise. To facilitate practical applications, a novel optical remote sensing of the bi-directional learning object detection (ORS-BLOD) is proposed in this paper. In the method, the positive direction mechanism contains two feature re-identification convolutional modules, which can effectively distinguish complex internal texture features and improve the accuracy of small objects. The method further designs a novel auxiliary-point balancing IoU (ABIoU) loss in the reverse direction mechanism. The novel loss not only can avoid the local optimum solutions of Euclidean distance term in single-pair points regression but also can avoid IoU loss non-converging for local aspect ratio, which can realize the stability of the loss values and the direct measure of the side length. During the training phase, ABIoU loss does not produce additional parameters and improves the accuracy of box position and the integrity of aspect ratio. mAP50 of our method can, respectively, reach 73.3%, 87.03% and 56.84% on DIOR, DIOR6 and VOC2007 object detection data sets, and the high-precision and portability of our method are revealed by extensive experiment results and analysis. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 |
High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment |
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https://dx.doi.org/10.1007/s00371-023-03086-4 |
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Tang, Zefang Zhu, Yanmin |
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10.1007/s00371-023-03086-4 |
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2024-07-03T20:28:31.402Z |
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| score |
7.3996534 |