Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images
文献类型：期刊论文
| 作者 | Ding, Kun1 ; He, Guojin1; Gu, Huxiang2; Zhong, Zisha2; Xiang, Shiming2; Pan, Chunhong2
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| 出版日期 | 2022 |
| 卷号 | 19页码:5 |
| 关键词 | Convolution Head Training Testing Object detection Feature extraction Real-time systems Aerial images object detection sparse convolution spatial sparsity |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2020.3035844 |
| 通讯作者 | Ding, Kun(kding1225@gmail.com) |
| 英文摘要 | Applications of aerial imaging, especially based on unmanned aerial vehicles (UAVs) platform, rapidly explode in recent years. Meanwhile, vision-based sensing, e.g., detection and recognition, for UAVs becomes increasingly important. Objects in aerial images are usually of tiny size, hence occupying a limited area. Terminology speaking, the images are very sparse in spatial. However, existing work in aerial object detection commonly ignores this point. Conversely, we explore the availability of such a property in improving the detection performance of aerial images. Specifically, we propose a general method, train in dense and test in sparse (TDTS), to exploit sparsity in aerial object detection: 1) in the training stage, the possible positions of object are learned by training a fully convolutional network (called prophet head) and 2) in the testing stage, prophet head identifies the possible object locations to reduce redundant computation in classification and box prediction head by sparse convolution. By extensive experiments on the VisDrone2019-Det data set, we find that the sparsity can not only help to speed up inference but also to improve accuracy. Thus, we argue that the sparsity deserves more attention. |
| WOS关键词 | VEHICLE DETECTION |
| 资助项目 | National Natural Science Foundation of China[61731022] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19090300] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000733952000060 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47148]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 通讯作者 | Ding, Kun |
| 作者单位 | 1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ding, Kun,He, Guojin,Gu, Huxiang,et al. Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5. |
| APA | Ding, Kun,He, Guojin,Gu, Huxiang,Zhong, Zisha,Xiang, Shiming,&Pan, Chunhong.(2022).Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5. |
| MLA | Ding, Kun,et al."Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5. |
入库方式： OAI收割
来源：自动化研究所
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