Using Deep Networks for Drone Detection

TL;DR

This paper presents a deep learning-based method for drone detection in videos, utilizing an innovative data augmentation technique to improve detection accuracy with limited real data.

Contribution

The study introduces an end-to-end CNN model for drone detection and a novel algorithm for generating large artificial datasets from real images.

Findings

High precision and recall achieved

Effective data augmentation for scarce datasets

End-to-end CNN model outperforms traditional methods

Abstract

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.