E2CropDet: An efficient end-to-end solution to crop row detection

Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Dongfang [verfasserIn] Li, Boliao [verfasserIn] Kang, Shuo [verfasserIn] Feng, Huaiqu [verfasserIn] Long, Sifang [verfasserIn] Wang, Jun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Machine vision Autonomous navigation Crop row detection Deep learning Image processing

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 227
Übergeordnetes Werk:	volume:227

DOI / URN:	10.1016/j.eswa.2023.120345

Katalog-ID:	ELV010170243

Internformat


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520			\|a Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second.
650		4	\|a Machine vision
650		4	\|a Autonomous navigation
650		4	\|a Crop row detection
650		4	\|a Deep learning
650		4	\|a Image processing
700	1		\|a Li, Boliao \|e verfasserin \|4 aut
700	1		\|a Kang, Shuo \|e verfasserin \|4 aut
700	1		\|a Feng, Huaiqu \|e verfasserin \|4 aut
700	1		\|a Long, Sifang \|e verfasserin \|4 aut
700	1		\|a Wang, Jun \|e verfasserin \|0 (orcid)0000-0001-5767-6149 \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 227 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
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936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
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Indexfelder

author_variant	d l dl b l bl s k sk h f hf s l sl j w jw
matchkey_str	lidongfangliboliaokangshuofenghuaiqulong:2023----:2rpeaefcetntedouino
hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.eswa.2023.120345 doi (DE-627)ELV010170243 (ELSEVIER)S0957-4174(23)00847-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Li, Dongfang verfasserin aut E2CropDet: An efficient end-to-end solution to crop row detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second. Machine vision Autonomous navigation Crop row detection Deep learning Image processing Li, Boliao verfasserin aut Kang, Shuo verfasserin aut Feng, Huaiqu verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 227 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 227
spelling	10.1016/j.eswa.2023.120345 doi (DE-627)ELV010170243 (ELSEVIER)S0957-4174(23)00847-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Li, Dongfang verfasserin aut E2CropDet: An efficient end-to-end solution to crop row detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second. Machine vision Autonomous navigation Crop row detection Deep learning Image processing Li, Boliao verfasserin aut Kang, Shuo verfasserin aut Feng, Huaiqu verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 227 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 227
allfields_unstemmed	10.1016/j.eswa.2023.120345 doi (DE-627)ELV010170243 (ELSEVIER)S0957-4174(23)00847-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Li, Dongfang verfasserin aut E2CropDet: An efficient end-to-end solution to crop row detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second. Machine vision Autonomous navigation Crop row detection Deep learning Image processing Li, Boliao verfasserin aut Kang, Shuo verfasserin aut Feng, Huaiqu verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 227 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 227
allfieldsGer	10.1016/j.eswa.2023.120345 doi (DE-627)ELV010170243 (ELSEVIER)S0957-4174(23)00847-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Li, Dongfang verfasserin aut E2CropDet: An efficient end-to-end solution to crop row detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second. Machine vision Autonomous navigation Crop row detection Deep learning Image processing Li, Boliao verfasserin aut Kang, Shuo verfasserin aut Feng, Huaiqu verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 227 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 227
allfieldsSound	10.1016/j.eswa.2023.120345 doi (DE-627)ELV010170243 (ELSEVIER)S0957-4174(23)00847-3 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Li, Dongfang verfasserin aut E2CropDet: An efficient end-to-end solution to crop row detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second. Machine vision Autonomous navigation Crop row detection Deep learning Image processing Li, Boliao verfasserin aut Kang, Shuo verfasserin aut Feng, Huaiqu verfasserin aut Long, Sifang verfasserin aut Wang, Jun verfasserin (orcid)0000-0001-5767-6149 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 227 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 227
language	English
source	Enthalten in Expert systems with applications 227 volume:227
sourceStr	Enthalten in Expert systems with applications 227 volume:227
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Machine vision Autonomous navigation Crop row detection Deep learning Image processing
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Li, Dongfang @@aut@@ Li, Boliao @@aut@@ Kang, Shuo @@aut@@ Feng, Huaiqu @@aut@@ Long, Sifang @@aut@@ Wang, Jun @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV010170243
language_de	englisch
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author	Li, Dongfang
spellingShingle	Li, Dongfang ddc 004 bkl 54.72 misc Machine vision misc Autonomous navigation misc Crop row detection misc Deep learning misc Image processing E2CropDet: An efficient end-to-end solution to crop row detection
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topic_title	004 VZ 54.72 bkl E2CropDet: An efficient end-to-end solution to crop row detection Machine vision Autonomous navigation Crop row detection Deep learning Image processing
topic	ddc 004 bkl 54.72 misc Machine vision misc Autonomous navigation misc Crop row detection misc Deep learning misc Image processing
topic_unstemmed	ddc 004 bkl 54.72 misc Machine vision misc Autonomous navigation misc Crop row detection misc Deep learning misc Image processing
topic_browse	ddc 004 bkl 54.72 misc Machine vision misc Autonomous navigation misc Crop row detection misc Deep learning misc Image processing
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title	E2CropDet: An efficient end-to-end solution to crop row detection
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title_full	E2CropDet: An efficient end-to-end solution to crop row detection
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title_sort	e2cropdet: an efficient end-to-end solution to crop row detection
title_auth	E2CropDet: An efficient end-to-end solution to crop row detection
abstract	Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second.
abstractGer	Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second.
abstract_unstemmed	Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A compact and efficient deep learning-based crop row detection network, named E2CropDet, is proposed in this study. E2CropDet models each crop row as a complete and independent object to enable an end-to-end detection manner with no involvement of image post-processing. Generic backbones are used as feature extractors in E2CropDet. Line-shaped proposals are developed as pre-defined detection anchors based on the shape characteristics and distribution pattern of crop rows. The feature vector obtained by pooling along the slender crop rows is fed into fully connected layers after aggregating contextual information. Then the final centreline extraction results are obtained by classification and regression. With ResNet-34 as the backbone, the proposed model results in a lateral deviation of 5.945 pixels for centerline extraction, which exceeds the semantic segmentation-based (7.153) and the Hough transform-based (17.834) approaches. Additionally, benefiting from an end-to-end pipeline that requires no post-process, it achieves a remarkable detection speed of 166 frames per second.
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title_short	E2CropDet: An efficient end-to-end solution to crop row detection
remote_bool	true
author2	Li, Boliao Kang, Shuo Feng, Huaiqu Long, Sifang Wang, Jun
author2Str	Li, Boliao Kang, Shuo Feng, Huaiqu Long, Sifang Wang, Jun
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doi_str	10.1016/j.eswa.2023.120345
up_date	2024-07-06T17:03:57.389Z
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