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DFF-ResNet : An Insect Pest Recognition Model Based on Residual Networks
https://tokushima-u.repo.nii.ac.jp/records/2008802
https://tokushima-u.repo.nii.ac.jp/records/2008802951a9a80-5fb8-404f-85d5-b933f37d05be
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
bdma_3_4_300.pdf (10.4 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||
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| 公開日 | 2021-06-11 | |||||||||||||||
| アクセス権 | ||||||||||||||||
| アクセス権 | open access | |||||||||||||||
| 資源タイプ | ||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||
| 資源タイプ | journal article | |||||||||||||||
| 出版社版DOI | ||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||
| 関連識別子 | https://doi.org/10.26599/BDMA.2020.9020021 | |||||||||||||||
| 言語 | ja | |||||||||||||||
| 関連名称 | 10.26599/BDMA.2020.9020021 | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | DFF-ResNet : An Insect Pest Recognition Model Based on Residual Networks | |||||||||||||||
| 言語 | en | |||||||||||||||
| 著者 |
Liu, Wenjie
× Liu, Wenjie
× Wu, Guoqing
× 任, 福継
WEKO
401
× 康, 鑫 |
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||
| 内容記述 | Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1×1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods. | |||||||||||||||
| 言語 | en | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | insect pest recognition | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | deep feature fusion | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | residual network | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | image classification | |||||||||||||||
| 書誌情報 |
en : Big Data Mining and Analytics 巻 3, 号 4, p. 300-310, 発行日 2020-11-16 |
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| 収録物ID | ||||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||||
| 収録物識別子 | 20960654 | |||||||||||||||
| 出版者 | ||||||||||||||||
| 出版者 | IEEE | |||||||||||||||
| 言語 | en | |||||||||||||||
| 権利情報 | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 権利情報 | The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). | |||||||||||||||
| EID | ||||||||||||||||
| 識別子 | 372392 | |||||||||||||||
| 識別子タイプ | URI | |||||||||||||||
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| 言語 | eng | |||||||||||||||
