Anchor-based Detection
Anchor-Based Detection is a detection methodology in object detection that generates region proposals by evaluating predefined anchor boxes at various locations, scales, and aspect ratios across the image, distinguishing itself from anchor-free approaches by relying on these parameterized reference boxes to predict object boundaries.
384
Publications
30.7K
Citations
1.6K
Authors
398
Institutions
Publications per year
2017–2026
366
Parents
384
RepPoints: Point Set Representation for Object Detection
Ze Yang, Shaohui Liu, Han Hu et al. · 2019 · 1.2K citations
Align Deep Features for Oriented Object Detection
Jiaming Han, Jian Ding, Jie Li et al. · IEEE Transactions on Geoscience and Remote Sensing · 2021 · 957 citations
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Xue Yang, Jirui Yang, Junchi Yan et al. · 2019 · 914 citations
FoveaBox: Beyound Anchor-Based Object Detection
IEEE Transactions on Image Processing · 2020 · 907 citations · Full text
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398
University of Chinese Academy of Sciences
Beijing, China
25 h-index
99 publications
15.8K citations

Chinese Academy of Sciences
Beijing, China
24 h-index
115 publications
11.1K citations

Beijing Institute of Technology
Beijing, China
14 h-index
58 publications
4.2K citations

Beihang University
Beijing, China
14 h-index
44 publications
2.5K citations

Xidian University
Xi'an, China
13 h-index
65 publications
2.8K citations

National University of Defense Technology
Changsha, China
12 h-index
55 publications
2K citations
Wuhan University
Wuhan, China
11 h-index
44 publications
6.2K citations
Tsinghua University
Beijing, China
11 h-index
22 publications
4.8K citations
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Repository · 56 publications · 3.7K citations
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32 publications · 3.7K citations
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31 publications · 1.8K citations
21
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17 publications · 693 citations
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GRSL · 13 publications · 288 citations
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