Drone Detection Using Convolutional Neural Networks

TL;DR

This paper explores drone detection using fisheye camera images and compares CNN, SVM, and nearest neighbor classifiers, demonstrating CNN's superior accuracy in identifying UAVs.

Contribution

It introduces a drone detection method employing CNN, SVM, and nearest neighbor classifiers with a focus on fisheye camera imagery, highlighting CNN's effectiveness.

Findings

CNN achieved 95% accuracy in drone detection.

SVM achieved 88% accuracy.

Nearest neighbor achieved 80% accuracy.

Abstract

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.