Track Boosting and Synthetic Data Aided Drone Detection

TL;DR

This paper presents a drone detection method that combines fine-tuned YOLOv5, synthetic data augmentation, and Kalman-based tracking to improve detection accuracy under challenging conditions.

Contribution

It introduces a novel approach integrating synthetic data and temporal tracking with YOLOv5 for enhanced drone detection performance.

Findings

Synthetic data augmentation improves detection accuracy.

Temporal tracking boosts detection confidence.

Method wins first place in AVSS 2021 challenge.

Abstract

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.