Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
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Object Detection in Aerial Images : A Large-Scale Benchmark and Challenges. / Ding, Jian; Xue, Nan; Xia, Gui Song; Bai, Xiang; Yang, Wen; Yang, Michael; Belongie, Serge; Luo, Jiebo; Datcu, Mihai; Pelillo, Marcello; Zhang, Liangpei.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Object Detection in Aerial Images
T2 - A Large-Scale Benchmark and Challenges
AU - Ding, Jian
AU - Xue, Nan
AU - Xia, Gui Song
AU - Bai, Xiang
AU - Yang, Wen
AU - Yang, Michael
AU - Belongie, Serge
AU - Luo, Jiebo
AU - Datcu, Mihai
AU - Pelillo, Marcello
AU - Zhang, Liangpei
N1 - Publisher Copyright: IEEE
PY - 2021
Y1 - 2021
N2 - In the 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.
AB - In the 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.
KW - aerial images
KW - benchmark dataset
KW - Codes
KW - Earth
KW - Libraries
KW - Object detection
KW - oriented object detection
KW - remote sensing
KW - Software
KW - Software algorithms
KW - Task analysis
UR - http://www.scopus.com/inward/record.url?scp=85119589943&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2021.3117983
DO - 10.1109/TPAMI.2021.3117983
M3 - Journal article
C2 - 34613910
AN - SCOPUS:85119589943
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
SN - 0162-8828
ER -
ID: 301817305