Towards densely clustered tiny pest detection in the wild environment
文献类型：期刊论文
| 作者 | Du, Jianming2; Liu, Liu3; Li, Rui2; Jiao, Lin4; Xie, Chengjun2 ; Wang, Rujing1,2
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2022-06-14 |
| 卷号 | 490 |
| 关键词 | Pest detection Clustered tiny object Small object detection Image dataset |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2021.12.012 |
| 通讯作者 | Liu, Liu(liuliu1993@sjtu.edu.cn) |
| 英文摘要 | Our life is populated with many small-size objects, such as human in aerial images and tiny pests in agriculture. Current generic and small object detection methods are only focus on tackling their sizes rather than distribution. Considering this limitation, we state a Densely Clustered Tiny (DCT) object detection problem using a novel metric Object Density Level (ODL) to measure the object distribution in an image. The DCT problem allows varied densely distributed objects in the real-world captured images. In dealing with the DCT problem, we select two kinds of aphids that usually gather into cliques in the real-world agricultural environment, and build an aphid dataset APHID-4K in our task. Accompanying the DCT task, we propose a novel DCT detection network (DCTDet) to address this challenge. Specifically, a Cluster Region Proposal Network (ClusRPN) is trained to select appropriate densely distributed object cluster regions from images. These candidates are classified into different groups according to their density. A Density Merging and Partition module (DMP) merges and partitions them respectively and finally outputs cluster regions with uniform size and density to a subsequent Local Detector Group (LDG). In addition, we also use Composited Cluster data Generation (CCG) to present a large-scale dataset for ClusRPN optimization for robust training procedure and theoretically analyze their effects in detail. Experiments on APHID-4K and another clustered small object detection dataset VisDrone show that our DCTDet achieves state-of-the-art performance. |
| WOS关键词 | CLASSIFICATION |
| 资助项目 | National Natural Science Foun-dation of China[32171888] ; National Natural Science Foun-dation of China[31671586] ; Youth Foundation of Natural Science Foundation of Anhui Province[1908085QE202] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000796255500005 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foun-dation of China ; Youth Foundation of Natural Science Foundation of Anhui Province |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/130879]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Liu, Liu |
| 作者单位 | 1.Univ Sci & Technol China, Hefei, Peoples R China 2.Chinese Acad Sci, Hefei Inst Intelligent Machines, Beijing, Peoples R China 3.Shanghai Jiao Tong Univ, Shanghai, Peoples R China 4.Anhui Univ, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Du, Jianming,Liu, Liu,Li, Rui,et al. Towards densely clustered tiny pest detection in the wild environment[J]. NEUROCOMPUTING,2022,490. |
| APA | Du, Jianming,Liu, Liu,Li, Rui,Jiao, Lin,Xie, Chengjun,&Wang, Rujing.(2022).Towards densely clustered tiny pest detection in the wild environment.NEUROCOMPUTING,490. |
| MLA | Du, Jianming,et al."Towards densely clustered tiny pest detection in the wild environment".NEUROCOMPUTING 490(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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