# Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks

## TL;DR

This paper presents a deep learning-based system for visible and thermal drone detection and tracking, utilizing novel data augmentation techniques to overcome limited training data, and demonstrating effective real-world performance.

## Contribution

It introduces two innovative data augmentation methods for thermal and visible drone images and integrates detection and tracking modules into a system that outperforms individual components.

## Key findings

- System performs well on real-world complex backgrounds
- Synthetic training data effectively generalizes to real images
- Proposed methods improve thermal drone detection accuracy

## Abstract

This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that even being trained on synthetic data, the proposed system performs well on real-world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.