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Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection

文献类型：期刊论文

作者Jiao, Lin3,4; Li, Gaoqiang3; Chen, Peng1,3; Wang, Rujing1,2,4; Du, Jianming4; Liu, Haiyun2,4; Dong, Shifeng2,4
刊名FRONTIERS IN PLANT SCIENCE
出版日期2022-06-02
卷号13
ISSN号1664-462X
关键词deep learning convolutional neural network deformable residual network agricultural pest target detection
DOI10.3389/fpls.2022.895944
通讯作者Jiao, Lin(ljiao@ahu.edu.cn) ; Chen, Peng(pengchen@ustc.edu) ; Wang, Rujing(rjwang@iim.ac.cn)
英文摘要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.
资助项目Natural Science Foundation of Anhui Higher Education Institutions of China[KJ2021A0025] ; National Natural Science Foundation of China[62072002] ; Major Special Science and Technology Project of Anhui Province[202003A06020016] ; Special Fund for Anhui Agriculture Research System
WOS研究方向Plant Sciences
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000812028800001
资助机构Natural Science Foundation of Anhui Higher Education Institutions of China ; National Natural Science Foundation of China ; Major Special Science and Technology Project of Anhui Province ; Special Fund for Anhui Agriculture Research System
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/131337]  
专题中国科学院合肥物质科学研究院
通讯作者Jiao, Lin; Chen, Peng; Wang, Rujing
作者单位1.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei, Peoples R China
2.Univ Sci & Technol China, Sci Isl Branch, Hefei, Peoples R China
3.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applica, Sch Internet, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei, Peoples R China
4.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei, Peoples R China
推荐引用方式
GB/T 7714		 Jiao, Lin,Li, Gaoqiang,Chen, Peng,et al. Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection[J]. FRONTIERS IN PLANT SCIENCE,2022,13.
APA Jiao, Lin.,Li, Gaoqiang.,Chen, Peng.,Wang, Rujing.,Du, Jianming.,...&Dong, Shifeng.(2022).Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection.FRONTIERS IN PLANT SCIENCE,13.
MLA Jiao, Lin,et al."Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection".FRONTIERS IN PLANT SCIENCE 13(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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