MD-YOLO: Multi-scale Dense YOLO for small target pest detection
文献类型：期刊论文
| 作者 | Tian, Yunong3 ; Wang, Shihui1; Li, En3; Yang, Guodong3; Liang, Zize2; Tan, Min2
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| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 213页码:12 |
| ISSN号 | 0168-1699 |
| 关键词 | Pest detection Deep learning Computer vision Internet of Things Precision agriculture |
| DOI | 10.1016/j.compag.2023.108233 |
| 通讯作者 | Tian, Yunong(yunong.tian@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | PLANTHOPPERS |
| 资助项目 | National Natural Science Founda-tion of China[62206275] |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001079186800001 |
| 资助机构 | National Natural Science Founda-tion of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53056]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Tian, Yunong |
| 作者单位 | 1.Aerosp Shenzhou Smart Syst Technol Co Ltd, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Engn Lab Intelligent Ind Vis, 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. |
入库方式： OAI收割
来源：自动化研究所
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