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
Hauptverfasser: Ding, Jian, Xue, Nan, Xia, Gui-Song, Bai, Xiang, Yang, Wen, Yang, Michael Ying, Belongie, Serge, Luo, Jiebo, Datcu, Mihai, Pelillo, Marcello, Zhang, Liangpei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
aerial images
Aerial patrol
Algorithms
Annotations
benchmark dataset
Benchmarks
Codes
Datasets
Earth
Libraries
Object detection
Object recognition
oriented object detection
remote sensing
Software
Software algorithms
Task analysis
Websites
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung: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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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3117983