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Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition

文献类型：期刊论文

作者Wang, Fangyuan1,3; Wang, Rujing3; Xie, Chengjun3; Yang, Po2; Liu, Liu1,3
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2020-02-01
卷号169
关键词Convolutional neural network Context-aware attention network Multi-projection pest detection model In-field pest in food crop
ISSN号0168-1699
DOI10.1016/j.compag.2020.105222
通讯作者Wang, Rujing(rjwang@iim.ac.cn) ; Xie, Chengjun(cjxie@iim.ac.cn) ; Yang, Po(po.yang@sheffield.ac.uk)
英文摘要Automatic in-field pest detection and recognition using mobile vision technique is a hot topic in modern intelligent agriculture, but suffers from serious challenges including complexity of wild environment, detection of tiny size pest and classification of multiple classes of pests. While recent deep learning based mobile vision techniques have shown some success in overcoming above issues, one key problem is that towards large-scale multiple species of pest data, imbalanced classes significantly reduce their detection and recognition accuracy. In this paper, we propose a novel two-stages mobile vision based cascading pest detection approach (DeepPest) towards large-scale multiple species of pest data. This approach firstly extracts multi-scale contextual information of the images as prior knowledge to build up a context-aware attention network for initial classification of pest images into crop categories. Then, a multi-projection pest detection model (MDM) is proposed and trained by crop-related pest images. The role of MDM can combine pest contextual information from low-level convolutional layers with these in high-level convolutional layers for generating the super-resolved feature. Finally, we utilize the attention mechanism and data augmentation to improve the effectiveness of in-field pest detection. We evaluate our method on our newly established large-scale dataset In-Field Pest in Food Crop (IPFC) and sufficient experimental results show that DeepPest proposed in this paper outperforms state-of-the-art object detection methods in detecting in-field pest.
资助项目National Key Technology R&D Program of China[2018YFD0200300] ; National Natural Science Foundation of China[31401293] ; National Natural Science Foundation of China[31671586] ; National Natural Science Foundation of China[61773360] ; Major Special Science and Technology Project of Anhui Province[201903a06020006]
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:000517665600048
出版者ELSEVIER SCI LTD
资助机构National Key Technology R&D Program of China ; National Natural Science Foundation of China ; Major Special Science and Technology Project of Anhui Province
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/103977]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Rujing; Xie, Chengjun; Yang, Po
作者单位1.Univ Sci & Technol China, Hefei 230026, Peoples R China
2.Univ Sheffield, Sheffield, S Yorkshire, England
3.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Fangyuan,Wang, Rujing,Xie, Chengjun,et al. Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2020,169.
APA Wang, Fangyuan,Wang, Rujing,Xie, Chengjun,Yang, Po,&Liu, Liu.(2020).Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition.COMPUTERS AND ELECTRONICS IN AGRICULTURE,169.
MLA Wang, Fangyuan,et al."Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition".COMPUTERS AND ELECTRONICS IN AGRICULTURE 169(2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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