An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands
Abstract Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Deshuai [verfasserIn] Yan, Jin [verfasserIn] Xue, Tao [verfasserIn] Liu, Yong [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 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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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 83(2023), 16 vom: 03. Nov., Seite 48701-48717 |
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| Übergeordnetes Werk: |
volume:83 ; year:2023 ; number:16 ; day:03 ; month:11 ; pages:48701-48717 |
| Links: |
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| DOI / URN: |
10.1007/s11042-023-17443-x |
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| Katalog-ID: |
SPR055769470 |
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| 520 | |a Abstract Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. | ||
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| 650 | 4 | |a Agricultural automation |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Xue, Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yong |e verfasserin |0 (orcid)0000-0003-4098-2339 |4 aut | |
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10.1007/s11042-023-17443-x doi (DE-627)SPR055769470 (SPR)s11042-023-17443-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zheng, Deshuai verfasserin aut An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands 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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. Crop row detection (dpeaa)DE-He213 Instance segmentation (dpeaa)DE-He213 Agricultural automation (dpeaa)DE-He213 Yan, Jin verfasserin aut Xue, Tao verfasserin aut Liu, Yong verfasserin (orcid)0000-0003-4098-2339 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 16 vom: 03. Nov., Seite 48701-48717 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:16 day:03 month:11 pages:48701-48717 https://dx.doi.org/10.1007/s11042-023-17443-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_2119 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 54.87 VZ AR 83 2023 16 03 11 48701-48717 |
| spelling |
10.1007/s11042-023-17443-x doi (DE-627)SPR055769470 (SPR)s11042-023-17443-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zheng, Deshuai verfasserin aut An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands 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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. Crop row detection (dpeaa)DE-He213 Instance segmentation (dpeaa)DE-He213 Agricultural automation (dpeaa)DE-He213 Yan, Jin verfasserin aut Xue, Tao verfasserin aut Liu, Yong verfasserin (orcid)0000-0003-4098-2339 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 16 vom: 03. Nov., Seite 48701-48717 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:16 day:03 month:11 pages:48701-48717 https://dx.doi.org/10.1007/s11042-023-17443-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_2119 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 54.87 VZ AR 83 2023 16 03 11 48701-48717 |
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10.1007/s11042-023-17443-x doi (DE-627)SPR055769470 (SPR)s11042-023-17443-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zheng, Deshuai verfasserin aut An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands 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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. Crop row detection (dpeaa)DE-He213 Instance segmentation (dpeaa)DE-He213 Agricultural automation (dpeaa)DE-He213 Yan, Jin verfasserin aut Xue, Tao verfasserin aut Liu, Yong verfasserin (orcid)0000-0003-4098-2339 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 16 vom: 03. Nov., Seite 48701-48717 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:16 day:03 month:11 pages:48701-48717 https://dx.doi.org/10.1007/s11042-023-17443-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_2119 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 54.87 VZ AR 83 2023 16 03 11 48701-48717 |
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10.1007/s11042-023-17443-x doi (DE-627)SPR055769470 (SPR)s11042-023-17443-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zheng, Deshuai verfasserin aut An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands 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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. Crop row detection (dpeaa)DE-He213 Instance segmentation (dpeaa)DE-He213 Agricultural automation (dpeaa)DE-He213 Yan, Jin verfasserin aut Xue, Tao verfasserin aut Liu, Yong verfasserin (orcid)0000-0003-4098-2339 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 16 vom: 03. Nov., Seite 48701-48717 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:16 day:03 month:11 pages:48701-48717 https://dx.doi.org/10.1007/s11042-023-17443-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_2119 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 54.87 VZ AR 83 2023 16 03 11 48701-48717 |
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10.1007/s11042-023-17443-x doi (DE-627)SPR055769470 (SPR)s11042-023-17443-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Zheng, Deshuai verfasserin aut An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands 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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. Crop row detection (dpeaa)DE-He213 Instance segmentation (dpeaa)DE-He213 Agricultural automation (dpeaa)DE-He213 Yan, Jin verfasserin aut Xue, Tao verfasserin aut Liu, Yong verfasserin (orcid)0000-0003-4098-2339 aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2023), 16 vom: 03. Nov., Seite 48701-48717 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:16 day:03 month:11 pages:48701-48717 https://dx.doi.org/10.1007/s11042-023-17443-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_2119 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 54.87 VZ AR 83 2023 16 03 11 48701-48717 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR055769470</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240508064706.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240508s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-023-17443-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR055769470</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-023-17443-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="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Deshuai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands</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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crop row detection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Instance segmentation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Agricultural automation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Jin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xue, Tao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-4098-2339</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">83(2023), 16 vom: 03. 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an instance segmentation framework based on parallelogram mask for crop row detection in various farmlands |
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An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands |
| abstract |
Abstract Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. © 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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. © 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 Crop row detection is one of the essential steps for autonomous guidance in agriculture. Conventional methods only detect the center lines of crop rows, without providing information about their widths and shapes, which cannot meet the growing demands. Instance segmentation, which segments each object with an individual pixel-wise mask, seems to be a more appropriate solution. However, universal instance segmentation methods usually detect with noise masks belonging to other crops or weeds. To address this issue, we propose a customized instance segmentation framework consisting of two steps. First, an adaptive deep neural network transforms the image into an approximate aerial view, in which the crop rows resemble parallelograms. Subsequently, we propose an instance segmentation approach called Parallelogram Mask (PlgMask) to segment the crop rows within the transformed image. We train and evaluate our method on the CRBD dataset Vidovíc et al. (Pattern Recognit 55:68–86, 2016), and the results show that it can accurately detect crop rows without noise masks. Additionally, we evaluate our method under the zero-shot setting, which demonstrates that the proposed method can achieve great performance even on an unseen dataset. © 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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| container_issue |
16 |
| title_short |
An instance segmentation framework based on parallelogram mask for crop row detection in various farmlands |
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https://dx.doi.org/10.1007/s11042-023-17443-x |
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Yan, Jin Xue, Tao Liu, Yong |
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10.1007/s11042-023-17443-x |
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2024-07-03T17:53:10.693Z |
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| score |
7.400959 |