Machine learning algorithms applied for drone detection and classification: benefits and challenges

Abstract

In recent years, the increasing use of drones for both commercial and recreational purposes has led to heightened concerns regarding airspace safety. To address these issues, machine learning (ML) based drone detection and classification have emerged. This study explores the potential of ML-based drone classification, utilizing technologies like radar, visual, acoustic, and radio-frequency sensing systems. It undertakes a comprehensive examination of the existing literature in this domain, with a focus on various sensing modalities and their respective technological implementations. The study indicates that ML-based drone classification is promising, with numerous successful individual contributions. It is crucial to note, however, that much of the research in this field is experimental, making it difficult to compare results from various articles. There is also a noteworthy lack of reference datasets to help in the evaluation of different solutions.