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

Bilal Taha, Abdulhadi Shoufan

Research output: Contribution to journalArticlepeer-review

214 Scopus citations


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.

Original languageBritish English
Article number8846214
Pages (from-to)138669-138682
Number of pages14
JournalIEEE Access
StatePublished - 2019


  • acoustics
  • drone classification
  • Drone detection
  • machine learning
  • radar
  • radio-frequency
  • vision


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