Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research

TLDR

Drone detection and classification research has emerged recently due to rapid drone proliferation and airspace safety risks, yet most studies are experimental, lack comparable outcomes, and there is no requirement‑driven specification or reference datasets. This paper reviews current literature on machine‑learning‑based drone detection and classification across multiple modalities. The review covers radar, visual, acoustic, and radio‑frequency sensing technologies. The study finds that machine‑learning classification of drones is promising, with many successful individual contributions.

Abstract

This paper presents a comprehensive review of current literature on drone detection and classification using machine learning with different modalities. This research area has emerged in the last few years due to the rapid development of commercial and recreational drones and the associated risk to airspace safety. Addressed technologies encompass radar, visual, acoustic, and radio-frequency sensing systems. The general finding of this study demonstrates that machine learning-based classification of drones seems to be promising with many successful individual contributions. However, most of the performed research is experimental and the outcomes from different papers can hardly be compared. A general requirement-driven specification for the problem of drone detection and classification is still missing as well as reference datasets which would help in evaluating different solutions.