# Drone Detection Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2107.01435 · HTML: https://mlsanity.com/paper/2107.01435/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Using Deep Networks for Drone Detection](https://mlsanity.com/paper/1706.05726/) (2017)

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

- [TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection](https://mlsanity.com/paper/2211.16317/) (2022)
- [Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models](https://mlsanity.com/paper/2404.01160/) (2024)
- [Advance and Refinement: The Evolution of UAV Detection and Classification Technologies](https://mlsanity.com/paper/2409.05985/) (2024)

## Abstract (arXiv)

In image processing, it is essential to detect and track air targets, especially UAVs. In this paper, we detect the flying drone using a fisheye camera. In the field of diagnosis and classification of objects, there are always many problems that prevent the development of rapid and significant progress in this area. During the previous decades, a couple of advanced classification methods such as convolutional neural networks and support vector machines have been developed. In this study, the drone was detected using three methods of classification of convolutional neural network (CNN), support vector machine (SVM), and nearest neighbor. The outcomes show that CNN, SVM, and nearest neighbor have total accuracy of 95%, 88%, and 80%, respectively. Compared with other classifiers with the same experimental conditions, the accuracy of the convolutional neural network classifier is satisfactory.