Abstract
Traffic controls in modern society are part of urban management. With the assistance of unmanned aerial vehicles (UAVs) equipped with mounted cameras, researchers could capture aerial (bird-view) images from appropriate altitude. The perspective in aerial images makes appearances of objects squat, although aerial images can supply more contextual information about the environment by a broader view angle, the object instances may be detected by mistake. This fact diminishes the aerial images that can be fed to a network with higher dimensions that increases the computational cost to prevent the diminishing of pixels belonging to small objects. To compare model performance on small objects with aerial images, this study trains and tests two object detectors, i.e. YOLOv4 and YOLOv3, on the AU-AIR dataset, and exploited the parameterization of YOLO based models for small object detection. Finally, the key numerical results and observations are presented.
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