Feature Reuse Residual Networks for Insect Pest Recognition

Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines fea...
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Autor*in:	Fuji Ren [verfasserIn] Wenjie Liu [verfasserIn] Guoqing Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Insect pest recognition feature reuse residual network

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 122758-122768
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:122758-122768

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2938194

Katalog-ID:	DOAJ051970783

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allfields	10.1109/ACCESS.2019.2938194 doi (DE-627)DOAJ051970783 (DE-599)DOAJd3f38298c0a14642972816cbceb03f59 DE-627 ger DE-627 rakwb eng TK1-9971 Fuji Ren verfasserin aut Feature Reuse Residual Networks for Insect Pest Recognition 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach. Insect pest recognition feature reuse residual network Electrical engineering. Electronics. Nuclear engineering Wenjie Liu verfasserin aut Guoqing Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 122758-122768 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:122758-122768 https://doi.org/10.1109/ACCESS.2019.2938194 kostenfrei https://doaj.org/article/d3f38298c0a14642972816cbceb03f59 kostenfrei https://ieeexplore.ieee.org/document/8819933/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 122758-122768
spelling	10.1109/ACCESS.2019.2938194 doi (DE-627)DOAJ051970783 (DE-599)DOAJd3f38298c0a14642972816cbceb03f59 DE-627 ger DE-627 rakwb eng TK1-9971 Fuji Ren verfasserin aut Feature Reuse Residual Networks for Insect Pest Recognition 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach. Insect pest recognition feature reuse residual network Electrical engineering. Electronics. Nuclear engineering Wenjie Liu verfasserin aut Guoqing Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 122758-122768 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:122758-122768 https://doi.org/10.1109/ACCESS.2019.2938194 kostenfrei https://doaj.org/article/d3f38298c0a14642972816cbceb03f59 kostenfrei https://ieeexplore.ieee.org/document/8819933/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 122758-122768
allfields_unstemmed	10.1109/ACCESS.2019.2938194 doi (DE-627)DOAJ051970783 (DE-599)DOAJd3f38298c0a14642972816cbceb03f59 DE-627 ger DE-627 rakwb eng TK1-9971 Fuji Ren verfasserin aut Feature Reuse Residual Networks for Insect Pest Recognition 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach. Insect pest recognition feature reuse residual network Electrical engineering. Electronics. Nuclear engineering Wenjie Liu verfasserin aut Guoqing Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 122758-122768 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:122758-122768 https://doi.org/10.1109/ACCESS.2019.2938194 kostenfrei https://doaj.org/article/d3f38298c0a14642972816cbceb03f59 kostenfrei https://ieeexplore.ieee.org/document/8819933/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 122758-122768
allfieldsGer	10.1109/ACCESS.2019.2938194 doi (DE-627)DOAJ051970783 (DE-599)DOAJd3f38298c0a14642972816cbceb03f59 DE-627 ger DE-627 rakwb eng TK1-9971 Fuji Ren verfasserin aut Feature Reuse Residual Networks for Insect Pest Recognition 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach. Insect pest recognition feature reuse residual network Electrical engineering. Electronics. Nuclear engineering Wenjie Liu verfasserin aut Guoqing Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 122758-122768 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:122758-122768 https://doi.org/10.1109/ACCESS.2019.2938194 kostenfrei https://doaj.org/article/d3f38298c0a14642972816cbceb03f59 kostenfrei https://ieeexplore.ieee.org/document/8819933/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 122758-122768
allfieldsSound	10.1109/ACCESS.2019.2938194 doi (DE-627)DOAJ051970783 (DE-599)DOAJd3f38298c0a14642972816cbceb03f59 DE-627 ger DE-627 rakwb eng TK1-9971 Fuji Ren verfasserin aut Feature Reuse Residual Networks for Insect Pest Recognition 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach. Insect pest recognition feature reuse residual network Electrical engineering. Electronics. Nuclear engineering Wenjie Liu verfasserin aut Guoqing Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 122758-122768 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:122758-122768 https://doi.org/10.1109/ACCESS.2019.2938194 kostenfrei https://doaj.org/article/d3f38298c0a14642972816cbceb03f59 kostenfrei https://ieeexplore.ieee.org/document/8819933/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 122758-122768
language	English
source	In IEEE Access 7(2019), Seite 122758-122768 volume:7 year:2019 pages:122758-122768
sourceStr	In IEEE Access 7(2019), Seite 122758-122768 volume:7 year:2019 pages:122758-122768
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Insect pest recognition feature reuse residual network Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Fuji Ren @@aut@@ Wenjie Liu @@aut@@ Guoqing Wu @@aut@@
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callnumber-first	T - Technology
author	Fuji Ren
spellingShingle	Fuji Ren misc TK1-9971 misc Insect pest recognition misc feature reuse misc residual network misc Electrical engineering. Electronics. Nuclear engineering Feature Reuse Residual Networks for Insect Pest Recognition
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topic_title	TK1-9971 Feature Reuse Residual Networks for Insect Pest Recognition Insect pest recognition feature reuse residual network
topic	misc TK1-9971 misc Insect pest recognition misc feature reuse misc residual network misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Insect pest recognition misc feature reuse misc residual network misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Insect pest recognition misc feature reuse misc residual network misc Electrical engineering. Electronics. Nuclear engineering
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title	Feature Reuse Residual Networks for Insect Pest Recognition
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title_full	Feature Reuse Residual Networks for Insect Pest Recognition
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title_auth	Feature Reuse Residual Networks for Insect Pest Recognition
abstract	Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach.
abstractGer	Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach.
abstract_unstemmed	Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach.
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title_short	Feature Reuse Residual Networks for Insect Pest Recognition
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up_date	2024-07-03T23:09:09.390Z
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