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

Azimi, Seyedmajid and Bahmanyar, Reza and Henry, Corentin and Kurz, Franz (2021) EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 6920-6927. ICPR 2020, 10.-15.01.2021, Milan/IT (virtual). doi: 10.1109/ICPR48806.2021.9412353.

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


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.

Item URL in elib:https://elib.dlr.de/137887/
Document Type:Conference or Workshop Item (Speech)
Title:EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.dehttps://orcid.org/0000-0002-6084-2272
Bahmanyar, Rezareza.bahmanyar (at) dlr.dehttps://orcid.org/0000-0002-6999-714X
Henry, CorentinCorentin.henry (at) dlr.dehttps://orcid.org/0000-0002-4330-3058
Kurz, Franzfranz.kurz (at) dlr.dehttps://orcid.org/0000-0003-1718-0004
Date:January 2021
Journal or Publication Title:2020 25th International Conference on Pattern Recognition (ICPR)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/ICPR48806.2021.9412353
Page Range:pp. 6920-6927
Keywords:Vehicle Detection, Aerial Imagery, Real-world Traffic Monitoring, Deep Learning
Event Title:ICPR 2020
Event Location:Milan/IT (virtual)
Event Type:international Conference
Event Dates:10.-15.01.2021
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF, V - D.MoVe
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Azimi, Seyedmajid
Deposited On:23 Nov 2020 13:32
Last Modified:25 Aug 2021 10:44

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