# A Deep Learning Approach to Drone Monitoring

arXiv: https://arxiv.org/abs/1712.00863 · HTML: https://mlsanity.com/paper/1712.00863/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 16 of 16)

- [MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats](https://mlsanity.com/paper/2402.03706/) (2024)
- [Sim2Air - Synthetic aerial dataset for UAV monitoring](https://mlsanity.com/paper/2110.05145/) (2021)
- [Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System](https://mlsanity.com/paper/2306.15767/) (2023)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection](https://mlsanity.com/paper/2411.05633/) (2024)
- [A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive Fusion](https://mlsanity.com/paper/2109.01800/) (2021)
- [DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data, Flexible Views, and Transformed Domains](https://mlsanity.com/paper/2412.04789/) (2024)
- [Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network](https://mlsanity.com/paper/2403.16669/) (2024)
- [YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection](https://mlsanity.com/paper/2406.11641/) (2024)
- [LRDDv2: Enhanced Long-Range Drone Detection Dataset with Range Information and Comprehensive Real-World Challenges](https://mlsanity.com/paper/2508.03331/) (2025)
- [Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments](https://mlsanity.com/paper/2509.14012/) (2025)
- [A Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles](https://mlsanity.com/paper/2511.18344/) (2025)
- [DronePose: The identification, segmentation, and orientation detection of drones via neural networks](https://mlsanity.com/paper/2112.05488/) (2021)
- [A drone detector with modified backbone and multiple pyramid featuremaps enhancement structure (MDDPE)](https://mlsanity.com/paper/2405.02882/) (2024)
- [SimD3: A Synthetic drone Dataset with Payload and Bird Distractor Modeling for Robust Detection](https://mlsanity.com/paper/2601.14742/) (2026)
- [SkyEV: RGB-Event UAV detection and tracking dataset and baseline](https://mlsanity.com/paper/2607.18747/) (2026)

## Abstract (arXiv)

A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drone's location. 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.