Gaining Scale Invariance in UAV Bird's Eye View Object Detection by Adaptive Resizing

In this work, we introduce a new preprocessing step applicable to UAV bird's eye view imagery, which we call Adaptive Resizing. It is constructed to adjust the vast variances in objects' scales, which are naturally inherent to UAV data sets. Furthermore, it improves inference speed by four to five times on average. We test this extensively on UAVDT, VisDrone, and on a new data set, we captured ourselves. On UAVDT, we achieve more than 100 % relative improvement in AP50. Moreover, we show how this method can be applied to a general UAV object detection task. Additionally, we successfully test our method on a domain transfer task where we train on some interval of altitudes and test on a different one. Code will be made available at our website.