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

Author / Creator

ICPR 2020 MAIN CONFERENCE (2021)

Conferences

ICPR 2020 MAIN CONFERENCE OS T1.8: Scene Analysis, Learning and Datasets (2021)

Available as

Online

Summary

Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the remote vision domains with applications in traffic monitoring and disaster management. In...

Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the 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. 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. Experiments show that the EAGLE dataset accurately reflects real-world situations and correspondingly challenging applications.

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