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From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images | |
Wu, Jingqian1; Xu, Shibiao2 | |
发表期刊 | REMOTE SENSING |
2021-07-01 | |
卷号 | 13期号:13页码:16 |
通讯作者 | Xu, Shibiao(shibiao.xu@nlpr.ia.ac.cn) |
摘要 | Accurate object detection is important in computer vision. However, detecting small objects in low-resolution images remains a challenging and elusive problem, primarily because these objects are constructed of less visual information and cannot be easily distinguished from similar background regions. To resolve this problem, we propose a Hierarchical Small Object Detection Network in low-resolution remote sensing images, named HSOD-Net. We develop a point-to-region detection paradigm by first performing a key-point prediction to obtain position hypotheses, then only later super-resolving the image and detecting the objects around those candidate positions. By postponing the object prediction to after increasing its resolution, the obtained key-points are more stable than their traditional counterparts based on early object detection with less visual information. This hierarchical approach, HSOD-Net, saves significant run-time, which makes it more suitable for practical applications such as search and rescue, and drone navigation. In comparison with the state-of-art models, HSOD-Net achieves remarkable precision in detecting small objects in low-resolution remote sensing images. |
关键词 | small object detection key-point prediction image enhancement low resolution |
DOI | 10.3390/rs13132620 |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000670955800001 |
出版者 | MDPI |
七大方向——子方向分类 | 三维视觉 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45278 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Xu, Shibiao |
作者单位 | 1.Wake Forest Univ, Dept Comp Sci, Winston Salem, NC 27019 USA 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wu, Jingqian,Xu, Shibiao. From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images[J]. REMOTE SENSING,2021,13(13):16. |
APA | Wu, Jingqian,&Xu, Shibiao.(2021).From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images.REMOTE SENSING,13(13),16. |
MLA | Wu, Jingqian,et al."From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images".REMOTE SENSING 13.13(2021):16. |
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