Deep Learning Based Automatic Multiclass Wild Pest Monitoring Approach Using Hybrid Global and Local Activated Features
文献类型：期刊论文
| 作者 | Liu, Liu1; Xie, Chengjun2 ; Wang, Rujing2; Yang, Po3; Sudirman, Sud4; Zhang, Jie2; Li, Rui2; Wang, Fangyuan1
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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| 出版日期 | 2021-11-01 |
| 卷号 | 17 |
| ISSN号 | 1551-3203 |
| 关键词 | Feature extraction Monitoring Machine learning Object detection Agriculture Proposals Image recognition Convolutional neural network (CNN) global activated feature pyramid network local activated region proposal network pest monitoring |
| DOI | 10.1109/TII.2020.2995208 |
| 通讯作者 | Xie, Chengjun(cjxie@iim.ac.cn) ; Wang, Rujing(rjwang@iim.ac.cn) |
| 英文摘要 | Specialized control of pests and diseases have been a high-priority issue for the agriculture industry in many countries. On account of automation and cost effectiveness, image analytic pest recognition systems are widely utilized in practical crops prevention applications. But due to powerless hand-crafted features, current image analytic approaches achieve low accuracy and poor robustness in practical large-scale multiclass pest detection and recognition. To tackle this problem, this article proposes a novel deep learning based automatic approach using hybrid and local activated features for pest monitoring. In the presented method, we exploit the global information from feature maps to build our global activated feature pyramid network to extract pests' highly discriminative features across various scales over both depth and position levels. It makes changes of depth or spatial sensitive features in pest images more visible during downsampling. Next, an improved pest localization module named local activated region proposal network is proposed to find the precise pest objects positions by augmenting contextualized and attentional information for feature completion and enhancement in local level. The approach is evaluated on our seven-year large-scale pest data-set containing 88.6 K images (16 types of pests) with 582.1 K manually labeled pest objects. The experimental results show that our solution performs over 75.03% mean average precision (mAP) in industrial circumstances, which outweighs two other state-of-the-art methods: Faster R-CNN with mAP up to 70% and feature pyramid network mAP up to 72%. |
| WOS关键词 | CLASSIFICATION ; NETWORK |
| 资助项目 | National Natural Science Foundation of China (NSFC)[61773360] ; National Natural Science Foundation of China (NSFC)[31671586] ; Major Special Science and Technology Project of Anhui Province[201903a06020006] ; Innovate U.K. (U.K.-China: Precision for Enhancing Agriculture Productivity)[671197] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000679533900038 |
| 资助机构 | National Natural Science Foundation of China (NSFC) ; Major Special Science and Technology Project of Anhui Province ; Innovate U.K. (U.K.-China: Precision for Enhancing Agriculture Productivity) |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/124281]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Xie, Chengjun; Wang, Rujing |
| 作者单位 | 1.Univ Sci & Technol China, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 3.Univ Sheffield, Dept Comp Sci, Sheffield S10 2TG, S Yorkshire, England 4.Liverpool John Moores Univ, Dept Comp Sci, Liverpool L3 5UG, Merseyside, England |
| 推荐引用方式 GB/T 7714 | Liu, Liu,Xie, Chengjun,Wang, Rujing,et al. Deep Learning Based Automatic Multiclass Wild Pest Monitoring Approach Using Hybrid Global and Local Activated Features[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2021,17. |
| APA | Liu, Liu.,Xie, Chengjun.,Wang, Rujing.,Yang, Po.,Sudirman, Sud.,...&Wang, Fangyuan.(2021).Deep Learning Based Automatic Multiclass Wild Pest Monitoring Approach Using Hybrid Global and Local Activated Features.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,17. |
| MLA | Liu, Liu,et al."Deep Learning Based Automatic Multiclass Wild Pest Monitoring Approach Using Hybrid Global and Local Activated Features".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 17(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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