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Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism

文献类型：期刊论文

作者Zhang, Wei2,3; Sun, Youqiang2; Huang, He2; Pei, Haotian2,3; Sheng, Jiajia2; Yang, Po1
刊名AGRICULTURE-BASEL
出版日期2022-08-01
卷号12
关键词early pest control pest region small object context attention mechanism feature fusion
DOI10.3390/agriculture12081104
通讯作者Huang, He(hhuang@iim.ac.cn)
英文摘要In precision agriculture, effective monitoring of corn pest regions is crucial to developing early scientific prevention strategies and reducing yield losses. However, complex backgrounds and small objects in real farmland bring challenges to accurate detection. In this paper, we propose an improved model based on YOLOv4 that uses contextual information and attention mechanism. Firstly, a context priming module with simple architecture is designed, where effective features of different layers are fused as additional context features to augment pest region feature representation. Secondly, we propose a multi-scale mixed attention mechanism (MSMAM) with more focus on pest regions and reduction of noise interference. Finally, the mixed attention feature-fusion module (MAFF) with MSMAM as the kernel is applied to selectively fuse effective information from additional features of different scales and alleviate the inconsistencies in their fusion. Experimental results show that the improved model performs better in different growth cycles and backgrounds of corn, such as corn in vegetative 12th, the vegetative tasseling stage, and the overall dataset. Compared with the baseline model (YOLOv4), our model achieves better average precision (AP) by 6.23%, 6.08%, and 7.2%, respectively. In addition, several comparative experiments were conducted on datasets with different corn growth cycles and backgrounds, and the results verified the effectiveness and usability of the proposed method for such tasks, providing technical reference and theoretical research for the automatic identification and control of pests.
资助项目National Key Research and Development Program of China[2021YFD200060102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28120400]
WOS研究方向Agriculture
语种英语
WOS记录号WOS:000846290200001
出版者MDPI
资助机构National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/131914]  
专题中国科学院合肥物质科学研究院
通讯作者Huang, He
作者单位1.Univ Sheffield, Dept Comp Sci, Sheffield S1 1DA, S Yorkshire, England
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
3.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Wei,Sun, Youqiang,Huang, He,et al. Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism[J]. AGRICULTURE-BASEL,2022,12.
APA Zhang, Wei,Sun, Youqiang,Huang, He,Pei, Haotian,Sheng, Jiajia,&Yang, Po.(2022).Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism.AGRICULTURE-BASEL,12.
MLA Zhang, Wei,et al."Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism".AGRICULTURE-BASEL 12(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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