MD-YOLO: Multi-scale Dense YOLO for small target pest detection

doi:10.1016/j.compag.2023.108233

CASIA OpenIR

	MD-YOLO: Multi-scale Dense YOLO for small target pest detection
	Tian, Yunong 1; Wang, Shihui 2; Li, En 1; Yang, Guodong 1; Liang, Zize 3; Tan, Min 3
发表期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
	2023-10-01
卷号	213 页码:12
通讯作者	Tian, Yunong(yunong.tian@ia.ac.cn)
摘要	The detection of pests plays a crucial role in intelligent early warning systems of injurious insects and diseases in precision agriculture. However, pests strong concealment and mobility pose significant challenges to their timely detection. In this paper, we propose a novel approach called Multi-scale Dense YOLO (MD-YOLO) for detecting three typical small target lepidopteran pests on sticky insect boards. In MD-YOLO, we design three key components: the image feature extraction part, the feature fusion network, and the prediction module. To enhance the utilization of feature maps and mitigate information loss, we incorporate DenseNet blocks and an adaptive attention module (AAM) into the feature extraction part. The AAM helps capture relevant image details and improves the model's ability to exploit feature representations effectively. For effective feature integration, our feature fusion network incorporates both a feature extraction path and a feature aggregation path. This enables the deep network to leverage spatial location information from the shallower network, thereby enhancing the detection accuracy. Experimental results demonstrate the effectiveness of MD-YOLO, with detection results achieving an mAP@.5 value of 86.2%, an F1 score of 79.1%, and an IoU value of 88.1%. We conduct extensive experiments to compare MD-YOLO with state-of-the-art models, and the results showcase its superiority. Furthermore, we design an Internet of Things (IoT) system that demonstrates MD-YOLO's performance in real-world field scenes, highlighting its practical applicability.
关键词	Pest detection Deep learning Computer vision Internet of Things Precision agriculture
DOI	10.1016/j.compag.2023.108233
关键词[WOS]	PLANTHOPPERS
收录类别	SCI
语种	英语
资助项目	National Natural Science Founda-tion of China[62206275]
项目资助者	National Natural Science Founda-tion of China
WOS研究方向	Agriculture ; Computer Science
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001079186800001
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53055
通讯作者	Tian, Yunong
作者单位	1.Chinese Acad Sci, Inst Automat, CAS Engn Lab Intelligent Ind Vis, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Aerosp Shenzhou Smart Syst Technol Co Ltd, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Tian, Yunong,Wang, Shihui,Li, En,et al. MD-YOLO: Multi-scale Dense YOLO for small target pest detection[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2023,213:12.
APA	Tian, Yunong,Wang, Shihui,Li, En,Yang, Guodong,Liang, Zize,&Tan, Min.(2023).MD-YOLO: Multi-scale Dense YOLO for small target pest detection.COMPUTERS AND ELECTRONICS IN AGRICULTURE,213,12.
MLA	Tian, Yunong,et al."MD-YOLO: Multi-scale Dense YOLO for small target pest detection".COMPUTERS AND ELECTRONICS IN AGRICULTURE 213(2023):12.