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

doi:10.3390/agriculture12081104

HFCAS OpenIR

	Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism
	Zhang, Wei 1,2; Sun, Youqiang2 ; Huang, He2 ; Pei, Haotian 1,2; Sheng, Jiajia 2; Yang, Po 3
	2022-08-01
发表期刊	AGRICULTURE-BASEL
通讯作者	Huang, He(hhuang@iim.ac.cn)
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.
关键词	early pest control pest region small object context attention mechanism feature fusion
DOI	10.3390/agriculture12081104
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021YFD200060102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28120400]
项目资助者	National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向	Agriculture
WOS类目	Agronomy
WOS记录号	WOS:000846290200001
出版者	MDPI
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/131915
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.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Univ Sheffield, Dept Comp Sci, Sheffield S1 1DA, S Yorkshire, England
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).