  In this paper we propose a novel approach to generate a synthetic aerial
dataset for application in UAV monitoring. We propose to accentuate shape-based
object representation by applying texture randomization. A diverse dataset with
photorealism in all parameters such as shape, pose, lighting, scale, viewpoint,
etc. except for atypical textures is created in a 3D modelling software
Blender. Our approach specifically targets two conditions in aerial images
where texture of objects is difficult to detect, namely challenging
illumination and objects occupying only a small portion of the image.
Experimental evaluation of YOLO and Faster R-CNN detectors trained on synthetic
data with randomized textures confirmed our approach by increasing the mAP
value (17 and 3.7 percentage points for YOLO; 20 and 1.1 percentage points for
Faster R-CNN) on two test datasets of real images, both containing UAV-to-UAV
images with motion blur. Testing on different domains, we conclude that the
more the generalisation ability is put to the test, the more apparent are the
advantages of the shape-based representation.
