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Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection
An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, th...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201688/ https://www.ncbi.nlm.nih.gov/pubmed/35720529 http://dx.doi.org/10.3389/fpls.2022.895944 |
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author | Jiao, Lin Li, Gaoqiang Chen, Peng Wang, Rujing Du, Jianming Liu, Haiyun Dong, Shifeng |
author_facet | Jiao, Lin Li, Gaoqiang Chen, Peng Wang, Rujing Du, Jianming Liu, Haiyun Dong, Shifeng |
author_sort | Jiao, Lin |
collection | PubMed |
description | An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, the precise detection of multi-classes pest faces great challenges. In this study, an end-to-end pest detection algorithm has been proposed on the basis of deep convolutional neural networks. The detection method adopts a deformable residual network to extract pest features and a global context-aware module for obtaining region-of-interests of agricultural pests. The detection results of the proposed method are compared with the detection results of other state-of-the-art methods, for example, RetinaNet, YOLO, SSD, FPN, and Cascade RCNN modules. The experimental results show that our method can achieve an average accuracy of 77.8% on 21 categories of agricultural pests. The proposed detection algorithm can achieve 20.9 frames per second, which can satisfy real-time pest detection. |
format | Online Article Text |
id | pubmed-9201688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92016882022-06-17 Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection Jiao, Lin Li, Gaoqiang Chen, Peng Wang, Rujing Du, Jianming Liu, Haiyun Dong, Shifeng Front Plant Sci Plant Science An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, the precise detection of multi-classes pest faces great challenges. In this study, an end-to-end pest detection algorithm has been proposed on the basis of deep convolutional neural networks. The detection method adopts a deformable residual network to extract pest features and a global context-aware module for obtaining region-of-interests of agricultural pests. The detection results of the proposed method are compared with the detection results of other state-of-the-art methods, for example, RetinaNet, YOLO, SSD, FPN, and Cascade RCNN modules. The experimental results show that our method can achieve an average accuracy of 77.8% on 21 categories of agricultural pests. The proposed detection algorithm can achieve 20.9 frames per second, which can satisfy real-time pest detection. Frontiers Media S.A. 2022-06-02 /pmc/articles/PMC9201688/ /pubmed/35720529 http://dx.doi.org/10.3389/fpls.2022.895944 Text en Copyright © 2022 Jiao, Li, Chen, Wang, Du, Liu and Dong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Jiao, Lin Li, Gaoqiang Chen, Peng Wang, Rujing Du, Jianming Liu, Haiyun Dong, Shifeng Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection |
title | Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection |
title_full | Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection |
title_fullStr | Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection |
title_full_unstemmed | Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection |
title_short | Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection |
title_sort | global context-aware-based deformable residual network module for precise pest recognition and detection |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201688/ https://www.ncbi.nlm.nih.gov/pubmed/35720529 http://dx.doi.org/10.3389/fpls.2022.895944 |
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