# Track Boosting and Synthetic Data Aided Drone Detection

arXiv: https://arxiv.org/abs/2111.12389 · HTML: https://mlsanity.com/paper/2111.12389/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)

## Built on by (most cited first, 3 of 3)

- [A review of UAV Visual Detection and Tracking Methods](https://mlsanity.com/paper/2306.05089/) (2023)
- [Sequence Models for Drone vs Bird Classification](https://mlsanity.com/paper/2207.10409/) (2022)
- [DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection](https://mlsanity.com/paper/2407.00830/) (2024)

## Abstract (arXiv)

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.