Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence Jg. 44; H. 11; S. 7778 - 7796 |
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| Hauptverfasser: | , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| Online-Zugang: | Volltext |
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| Abstract | In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images. |
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| AbstractList | In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images. In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird’s-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images. |
| Author | Luo, Jiebo Yang, Wen Zhang, Liangpei Bai, Xiang Belongie, Serge Xue, Nan Pelillo, Marcello Ding, Jian Xia, Gui-Song Datcu, Mihai Yang, Michael Ying |
| Author_xml | – sequence: 1 givenname: Jian orcidid: 0000-0002-7188-5884 surname: Ding fullname: Ding, Jian email: jian.ding@whu.edu.cn organization: State Key Laboratory LIESMARS, Wuhan University, Wuhan, Hubei, China – sequence: 2 givenname: Nan orcidid: 0000-0002-5449-8073 surname: Xue fullname: Xue, Nan email: xuenan@whu.edu.cn organization: National Engineering Research Center for Multimedia Software, School of Computer Science and Institute of Artificial Intelligence, Wuhan University, Wuhan, Hubei, China – sequence: 3 givenname: Gui-Song orcidid: 0000-0001-7660-6090 surname: Xia fullname: Xia, Gui-Song email: guisong.xia@whu.edu.cn organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Institute of Artificial Intelligence, China – sequence: 4 givenname: Xiang orcidid: 0000-0002-3449-5940 surname: Bai fullname: Bai, Xiang email: xbai@hust.edu.cn organization: School of Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei, China – sequence: 5 givenname: Wen orcidid: 0000-0002-3263-8768 surname: Yang fullname: Yang, Wen email: yangwen@whu.edu.cn organization: School of Electronic Information, China – sequence: 6 givenname: Michael Ying orcidid: 0000-0002-0649-9987 surname: Yang fullname: Yang, Michael Ying email: michael.yang@utwente.nl organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, NB, The Netherlands – sequence: 7 givenname: Serge orcidid: 0000-0002-0388-5217 surname: Belongie fullname: Belongie, Serge email: sjb344@cornell.edu organization: Department of Computer Science, Cornell University and Cornell Tech, Ithaca, NY, USA – sequence: 8 givenname: Jiebo orcidid: 0000-0002-4516-9729 surname: Luo fullname: Luo, Jiebo email: jluo@cs.rochester.edu organization: Department of Computer Science, University of Rochester, Rochester, NY, USA – sequence: 9 givenname: Mihai orcidid: 0000-0002-3477-9687 surname: Datcu fullname: Datcu, Mihai email: mihai.datcu@dlr.de organization: Remote Sensing Technology Institute, German Aerospace Center (DLR), Cologne, NRW, Germany – sequence: 10 givenname: Marcello orcidid: 0000-0001-8992-9243 surname: Pelillo fullname: Pelillo, Marcello email: pelillo@unive.it organization: DAIS, Ca'Foscari University of Venice, Venezia, Italy – sequence: 11 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei email: zlp62@whu.edu.cn organization: State Key Laboratory LIESMARS, Wuhan University, Wuhan, Hubei, China |
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