Pest24: A large-scale very small object data set of agricultural pests for multi-target detection | |
Wang, Qi-Jin2,3,4; Zhang, Sheng-Yu4; Dong, Shi-Feng4; Zhang, Guang-Cai1; Yang, Jin2; Li, Rui4; Wang, Hong-Qiang2,4 | |
刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
2020-08-01 | |
卷号 | 175 |
关键词 | Agricultural pests Data set Object detection Deep learning |
ISSN号 | 0168-1699 |
DOI | 10.1016/j.compag.2020.105585 |
通讯作者 | Wang, Hong-Qiang(hqwang@ustc.edu) |
英文摘要 | Precision agriculture poses new challenges for real-time monitoring pest population in field based on new-generation AI technology. In order to provide a big data resource for training pest detection deep learning models, this paper establishes a large-scale multi-target standardized data set of agricultural pests, named Pest24. Specifically, the data set currently consists of 25,378 field pest annotated images collected from our automatic pest trap & imaging device. Totally, 24 categories of typical pests are involved in Pest24, which dominantly destroy field crops in China every year. We apply several state-of-the-art deep learning detection methods, Faster RCNN, SSD, YOLOv3, Cascade R-CNN to detect the pests in the data set, and obtain encouraging results for real-time monitoring field crop pests. To explore the factors that affect the detection accuracy of pests, we analyze the data set in a variety of aspects, finding that three factors, i.e. relative scale, number of instances and object adhesion, mainly influence the pest detection performance. Overall, Pest24 is featured typically with large scale multi-pest image data, very small object scales, high object similarity and dense pest distribution. We hope that Pest24 promotes accurate multi-pest monitoring for precision agriculture and also benefits the machine vision community by providing a new specialized object detection benchmark. |
资助项目 | National Natural Science Foundation of China[61773360] ; National Natural Science Foundation of China[61973295] ; Anhui Province's Key Research and Development Project of China[201904a07020092] |
WOS关键词 | SIZE |
WOS研究方向 | Agriculture ; Computer Science |
语种 | 英语 |
出版者 | ELSEVIER SCI LTD |
WOS记录号 | WOS:000552020100023 |
资助机构 | National Natural Science Foundation of China ; Anhui Province's Key Research and Development Project of China |
内容类型 | 期刊论文 |
源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/43346] |
专题 | 合肥物质科学研究院_中科院合肥智能机械研究所 |
通讯作者 | Wang, Hong-Qiang |
作者单位 | 1.Anhui Normal Univ, Coll Comp & Informat, Wuhu 241002, Peoples R China 2.Anhui Xinhua Univ, Hefei 230088, Peoples R China 3.Univ Sci & Technol China, Hefei 230026, Peoples R China 4.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Qi-Jin,Zhang, Sheng-Yu,Dong, Shi-Feng,et al. Pest24: A large-scale very small object data set of agricultural pests for multi-target detection[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2020,175. |
APA | Wang, Qi-Jin.,Zhang, Sheng-Yu.,Dong, Shi-Feng.,Zhang, Guang-Cai.,Yang, Jin.,...&Wang, Hong-Qiang.(2020).Pest24: A large-scale very small object data set of agricultural pests for multi-target detection.COMPUTERS AND ELECTRONICS IN AGRICULTURE,175. |
MLA | Wang, Qi-Jin,et al."Pest24: A large-scale very small object data set of agricultural pests for multi-target detection".COMPUTERS AND ELECTRONICS IN AGRICULTURE 175(2020). |
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