  This is the paper for the first place winning solution of the Drone vs. Bird
Challenge, organized by AVSS 2021. As the usage of drones increases with
lowered costs and improved drone technology, drone detection emerges as a vital
object detection task. However, detecting distant drones under unfavorable
conditions, namely weak contrast, long-range, low visibility, requires
effective algorithms. Our method approaches the drone detection problem by
fine-tuning a YOLOv5 model with real and synthetically generated data using a
Kalman-based object tracker to boost detection confidence. Our results indicate
that augmenting the real data with an optimal subset of synthetic data can
increase the performance. Moreover, temporal information gathered by object
tracking methods can increase performance further.
