Track Boosting and Synthetic Data Aided Drone Detection
This addresses drone detection for security and surveillance applications, but it is incremental as it builds on existing methods like YOLOv5 and Kalman filters.
The paper tackles drone detection under challenging conditions like weak contrast and long-range by fine-tuning YOLOv5 with real and synthetic data and using a Kalman-based tracker to boost confidence, achieving first place in the Drone vs. Bird Challenge with performance increases from data augmentation and temporal information.
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.