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Deep Multibranch Fusion Residual Network for Insect Pest Recognition
https://tokushima-u.repo.nii.ac.jp/records/2009242
https://tokushima-u.repo.nii.ac.jp/records/20092421b713691-1a5d-42fd-921e-9c013f97dc07
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
tcds_13_3_705.pdf (1.2 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||
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| 公開日 | 2021-09-27 | |||||||||||||||
| 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 | ||||||||||||||||
| 関連識別子 | https://doi.org/10.1109/TCDS.2020.2993060 | |||||||||||||||
| 関連名称 | 10.1109/TCDS.2020.2993060 | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | AM | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | Deep Multibranch Fusion Residual Network for Insect Pest Recognition | |||||||||||||||
| 著者 |
Liu, Wenjie
× Liu, Wenjie
× Wu, Guoqing
× 任, 福継
WEKO
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| 抄録 | ||||||||||||||||
| 内容記述 | Earlier insect pest recognition is one of the critical factors for agricultural yield. Thus, an effective method to recognize the category of insect pests has become significant issues in the agricultural field. In this paper, we proposed a new residual block to learn multi-scale representation. In each block, it contains three branches: one is parameter-free, and the others contain several successive convolution layers. Moreover, we proposed a module and embedded it into the new residual block to recalibrate the channel-wise feature response and to model the relationship of the three branches. By stacking this kind of block, we constructed the Deep Multi-branch Fusion Residual Network (DMF-ResNet). For evaluating the model performance, we first test our model on CIFAR-10 and CIFAR-100 benchmark datasets. The experimental results show that DMF-ResNet outperforms the baseline models significantly. Then, we construct DMF-ResNet with different depths for high-resolution image classification tasks and apply it to recognize insect pests. We evaluate the model performance on the IP102 dataset, and the experimental results show that DMF-ResNet could achieve the best accuracy performance than the baseline models and other state-of-art methods. Based on these empirical experiments, we demonstrate the effectiveness of our approach. | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | Multi-branch Fusion | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | Insect pest recognition | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | image classification | |||||||||||||||
| 書誌情報 |
en : IEEE Transactions on Cognitive and Developmental Systems 巻 13, 号 3, p. 705-716, 発行日 2020-05-07 |
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| 収録物ID | ||||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||||
| 収録物識別子 | 23798939 | |||||||||||||||
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| 収録物識別子タイプ | ISSN | |||||||||||||||
| 収録物識別子 | 23798920 | |||||||||||||||
| 出版者 | ||||||||||||||||
| 出版者 | IEEE | |||||||||||||||
| 権利情報 | ||||||||||||||||
| 権利情報 | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
| EID | ||||||||||||||||
| 識別子 | 382154 | |||||||||||||||
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| 言語 | eng | |||||||||||||||
