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EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery

Azimi, Seyedmajid und Bahmanyar, Reza und Henry, Corentin und Kurz, Franz (2021) EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery. In: 25th International Conference on Pattern Recognition, ICPR 2020, Seiten 6920-6927. ICPR 2020, 2021-01-10 - 2021-01-15, Milan/IT (virtual). doi: 10.1109/ICPR48806.2021.9412353. ISBN 978-1-7281-8808-9. ISSN 1051-4651.

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Offizielle URL: https://ieeexplore.ieee.org/document/9412353

Kurzfassung

Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the near and remote vision domains with applications in traffic monitoring and disaster management. In the last decade, we have witnessed significant progress in object detection in ground imagery, but it is still in its infancy in airborne imagery, mostly due to the scarcity of diverse and large-scale datasets. Despite being a useful tool for different applications, current airborne datasets only partially reflect the challenges of real-world scenarios. To address this issue, we introduce EAGLE (oriEnted vehicle detection using Aerial imaGery in real-worLd scEnarios), a large-scale dataset for multi-class vehicle detection with object orientation information in aerial imagery. It features high-resolution aerial images composed of different real-world situations with a wide variety of camera sensor, resolution, flight altitude, weather, illumination, haze, shadow, time, city, country, occlusion, and camera angle. The annotation was done by airborne imagery experts with small- and large-vehicle classes. EAGLE contains 215,986 instances annotated with oriented bounding boxes defined by four points and orientation, making it by far the largest dataset to date in this task. It also supports researches on the haze and shadow removal as well as super-resolution and in-painting applications. We define three tasks: detection by (1) horizontal bounding boxes, (2) rotated bounding boxes, and (3) oriented bounding boxes. We carried out several experiments to evaluate several state-of-the-art methods in object detection on our dataset to form a baseline. Experiments show that the EAGLE dataset accurately reflects real-world situations and correspondingly challenging applications.

elib-URL des Eintrags:https://elib.dlr.de/137887/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.dehttps://orcid.org/0000-0002-6084-2272NICHT SPEZIFIZIERT
Bahmanyar, Rezareza.bahmanyar (at) dlr.dehttps://orcid.org/0000-0002-6999-714X146864875
Henry, CorentinCorentin.henry (at) dlr.dehttps://orcid.org/0000-0002-4330-3058NICHT SPEZIFIZIERT
Kurz, Franzfranz.kurz (at) dlr.dehttps://orcid.org/0000-0003-1718-0004NICHT SPEZIFIZIERT
Datum:Januar 2021
Erschienen in:25th International Conference on Pattern Recognition, ICPR 2020
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
DOI:10.1109/ICPR48806.2021.9412353
Seitenbereich:Seiten 6920-6927
ISSN:1051-4651
ISBN:978-1-7281-8808-9
Status:veröffentlicht
Stichwörter:Vehicle Detection, Aerial Imagery, Real-world Traffic Monitoring, Deep Learning
Veranstaltungstitel:ICPR 2020
Veranstaltungsort:Milan/IT (virtual)
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:10 Januar 2021
Veranstaltungsende:15 Januar 2021
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Straßenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V ST Straßenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - NGC KoFiF (alt), V - D.MoVe (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Azimi, Seyedmajid
Hinterlegt am:23 Nov 2020 13:32
Letzte Änderung:24 Apr 2024 20:39

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