  This work introduces a new preprocessing step for object detection applicable
to UAV bird's eye view imagery, which we call Adaptive Resizing. By design, it
helps alleviate the challenges coming with the vast variances in objects'
scales, naturally inherent to UAV data sets. Furthermore, it improves inference
speed by two to three times on average. We test this extensively on UAVDT,
VisDrone, and on a new data set we captured ourselves and achieve consistent
improvements while being considerably faster. Moreover, we show how to apply
this method to generic UAV object detection tasks. Additionally, we
successfully test our approach on a height transfer task where we train on some
interval of altitudes and test on a different one. Furthermore, we introduce a
small, fast detector meant for deployment to an embedded GPU. Code will be made
publicly available on our website.
