# Using Deep Networks for Drone Detection

arXiv: https://arxiv.org/abs/1706.05726 · HTML: https://mlsanity.com/paper/1706.05726/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2017.

## Built on by (most cited first, 9 of 9)

- [Sim2Air - Synthetic aerial dataset for UAV monitoring](https://mlsanity.com/paper/2110.05145/) (2021)
- [Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities](https://mlsanity.com/paper/2207.01927/) (2022)
- [Drone Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2107.01435/) (2021)
- [RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification](https://mlsanity.com/paper/2503.09033/) (2025)
- [YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs](https://mlsanity.com/paper/2512.18046/) (2025)
- [A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges](https://mlsanity.com/paper/2310.16360/) (2023)
- [GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark](https://mlsanity.com/paper/2305.03425/) (2023)
- [Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio](https://mlsanity.com/paper/2403.02080/) (2024)
- [Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis](https://mlsanity.com/paper/2403.17093/) (2024)

## Abstract (arXiv)

Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real images. With this approach, we can achieve precision and recall values both of which are high at the same time.