LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest

The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small target...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Xuewen [verfasserIn] Zhao, Qingzhan [verfasserIn] Jiang, Ping [verfasserIn] Zheng, Yuchen [verfasserIn] Yuan, Limengzi [verfasserIn] Yuan, Panli [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection

Übergeordnetes Werk:	Enthalten in: Computers and electronics in agriculture - Amsterdam [u.a.] : Elsevier Science, 1985, 198
Übergeordnetes Werk:	volume:198

DOI / URN:	10.1016/j.compag.2022.107035

Katalog-ID:	ELV058171584

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520			\|a The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest.
650		4	\|a Unmanned Aerial Vehicle (UAV)
650		4	\|a Visible light image
650		4	\|a Lightweight and Small Object Detection based YOLO (LDS-YOLO)
650		4	\|a Dead trees detection
700	1		\|a Zhao, Qingzhan \|e verfasserin \|4 aut
700	1		\|a Jiang, Ping \|e verfasserin \|4 aut
700	1		\|a Zheng, Yuchen \|e verfasserin \|4 aut
700	1		\|a Yuan, Limengzi \|e verfasserin \|4 aut
700	1		\|a Yuan, Panli \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Computers and electronics in agriculture \|d Amsterdam [u.a.] : Elsevier Science, 1985 \|g 198 \|h Online-Ressource \|w (DE-627)320567826 \|w (DE-600)2016151-7 \|w (DE-576)090955684 \|x 1872-7107 \|7 nnns
773	1	8	\|g volume:198
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936	b	k	\|a 48.03 \|j Methoden und Techniken der Land- und Forstwirtschaft \|q VZ
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Indexfelder

author_variant	x w xw q z qz p j pj y z yz l y ly p y py
matchkey_str	article:18727107:2022----::dylaihwihsalbeteetomtofredr
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publishDate	2022
allfields	10.1016/j.compag.2022.107035 doi (DE-627)ELV058171584 (ELSEVIER)S0168-1699(22)00352-0 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Wang, Xuewen verfasserin aut LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest. Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection Zhao, Qingzhan verfasserin aut Jiang, Ping verfasserin aut Zheng, Yuchen verfasserin aut Yuan, Limengzi verfasserin aut Yuan, Panli verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 198 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:198 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 198
spelling	10.1016/j.compag.2022.107035 doi (DE-627)ELV058171584 (ELSEVIER)S0168-1699(22)00352-0 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Wang, Xuewen verfasserin aut LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest. Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection Zhao, Qingzhan verfasserin aut Jiang, Ping verfasserin aut Zheng, Yuchen verfasserin aut Yuan, Limengzi verfasserin aut Yuan, Panli verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 198 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:198 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 198
allfields_unstemmed	10.1016/j.compag.2022.107035 doi (DE-627)ELV058171584 (ELSEVIER)S0168-1699(22)00352-0 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Wang, Xuewen verfasserin aut LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest. Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection Zhao, Qingzhan verfasserin aut Jiang, Ping verfasserin aut Zheng, Yuchen verfasserin aut Yuan, Limengzi verfasserin aut Yuan, Panli verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 198 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:198 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 198
allfieldsGer	10.1016/j.compag.2022.107035 doi (DE-627)ELV058171584 (ELSEVIER)S0168-1699(22)00352-0 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Wang, Xuewen verfasserin aut LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest. Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection Zhao, Qingzhan verfasserin aut Jiang, Ping verfasserin aut Zheng, Yuchen verfasserin aut Yuan, Limengzi verfasserin aut Yuan, Panli verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 198 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:198 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 198
allfieldsSound	10.1016/j.compag.2022.107035 doi (DE-627)ELV058171584 (ELSEVIER)S0168-1699(22)00352-0 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Wang, Xuewen verfasserin aut LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest. Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection Zhao, Qingzhan verfasserin aut Jiang, Ping verfasserin aut Zheng, Yuchen verfasserin aut Yuan, Limengzi verfasserin aut Yuan, Panli verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 198 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:198 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 198
language	English
source	Enthalten in Computers and electronics in agriculture 198 volume:198
sourceStr	Enthalten in Computers and electronics in agriculture 198 volume:198
format_phy_str_mv	Article
bklname	Methoden und Techniken der Land- und Forstwirtschaft
institution	findex.gbv.de
topic_facet	Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers and electronics in agriculture
authorswithroles_txt_mv	Wang, Xuewen @@aut@@ Zhao, Qingzhan @@aut@@ Jiang, Ping @@aut@@ Zheng, Yuchen @@aut@@ Yuan, Limengzi @@aut@@ Yuan, Panli @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320567826
dewey-sort	3620
id	ELV058171584
language_de	englisch
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author	Wang, Xuewen
spellingShingle	Wang, Xuewen ddc 620 bkl 48.03 misc Unmanned Aerial Vehicle (UAV) misc Visible light image misc Lightweight and Small Object Detection based YOLO (LDS-YOLO) misc Dead trees detection LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest
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topic_title	620 630 640 004 VZ 48.03 bkl LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest Unmanned Aerial Vehicle (UAV) Visible light image Lightweight and Small Object Detection based YOLO (LDS-YOLO) Dead trees detection
topic	ddc 620 bkl 48.03 misc Unmanned Aerial Vehicle (UAV) misc Visible light image misc Lightweight and Small Object Detection based YOLO (LDS-YOLO) misc Dead trees detection
topic_unstemmed	ddc 620 bkl 48.03 misc Unmanned Aerial Vehicle (UAV) misc Visible light image misc Lightweight and Small Object Detection based YOLO (LDS-YOLO) misc Dead trees detection
topic_browse	ddc 620 bkl 48.03 misc Unmanned Aerial Vehicle (UAV) misc Visible light image misc Lightweight and Small Object Detection based YOLO (LDS-YOLO) misc Dead trees detection
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title	LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest
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title_full	LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest
author_sort	Wang, Xuewen
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author_browse	Wang, Xuewen Zhao, Qingzhan Jiang, Ping Zheng, Yuchen Yuan, Limengzi Yuan, Panli
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author-letter	Wang, Xuewen
doi_str_mv	10.1016/j.compag.2022.107035
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title_sort	lds-yolo: a lightweight small object detection method for dead trees from shelter forest
title_auth	LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest
abstract	The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest.
abstractGer	The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest.
abstract_unstemmed	The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest.
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title_short	LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest
remote_bool	true
author2	Zhao, Qingzhan Jiang, Ping Zheng, Yuchen Yuan, Limengzi Yuan, Panli
author2Str	Zhao, Qingzhan Jiang, Ping Zheng, Yuchen Yuan, Limengzi Yuan, Panli
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doi_str	10.1016/j.compag.2022.107035
up_date	2024-07-06T18:15:02.101Z
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