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Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

Ding, Jian and Xue, Nan and Xia, Gui-Song and Yang, Wen and Ying Yang, Michael and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei (2022) Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TPAMI.2021.3117983. ISSN 0162-8828. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9560031

Abstract

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.

Item URL in elib:https://elib.dlr.de/144949/
Document Type:Article
Title:Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ding, JianWuhan UniversityUNSPECIFIED
Xue, NanWuhan UniversityUNSPECIFIED
Xia, Gui-SongWuhan UniversityUNSPECIFIED
Yang, WenWuhan UniversityUNSPECIFIED
Ying Yang, MichaelUniversity of TwenteUNSPECIFIED
Belongie, SergeCornell UniversityUNSPECIFIED
Luo, JieboRochester UniversityUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Pelillo, MarcelloUniversity of VeniceUNSPECIFIED
Zhang, LiangpeiWuhan UniversityUNSPECIFIED
Date:2022
Journal or Publication Title:IEEE Transactions on Pattern Analysis and Machine Intelligence
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/TPAMI.2021.3117983
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0162-8828
Status:In Press
Keywords:Object detection, remote sensing, aerial images, oriented object detection, benchmark dataset
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Otgonbaatar, Soronzonbold
Deposited On:29 Oct 2021 17:56
Last Modified:14 Jan 2022 14:28

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