A Comprehensive Survey of Unmanned Aerial Vehicles Wykrywanie and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions is an open-access multi-author work by Md Habibur Rahman, Mohammad Abrar Shakil Sejan, Md Abdul Aziz, Rana Tabassum, Jung-In Baik, and Hyoung-Kyu Song.

Autonomous unmanned aerial vehicles (UAVs) offer significant advantages across various fields, including disaster relief, aerial photography and videography, mapping and surveying, agriculture, and defense and public safety. However, the increasing likelihood of UAVs being misused to infiltrate critical locations such as airports and power plants poses serious public safety risks. Therefore, it is crucial to accurately and promptly identify different types of UAVs to prevent unauthorized access and mitigate security threats.

In recent years, machine learning (ML) algorithms have demonstrated significant potential in addressing these challenges by enabling the accurate detection and classification of UAVs over a wide range. This technology is considered highly promising for enhancing UAV systems. This survey reviews recent advancements in UAV detection and classification technologies based on ML and deep learning (DL) algorithms. The survey focuses on four primary types of ML-based UAV detection and classification technologies: radio frequency-based detection, visual data (images/video)-based detection, acoustic/sound-based detection, and radar-based detection.

Additionally, the authors explore hybrid sensor-based and reinforcement learning-based approaches for UAV detection and classification using ML. The report also discusses the challenges, solutions, and future research directions for ML-based UAV detection. Furthermore, it provides an in-depth exploration of datasets related to UAV detection and classification technologies. This survey is a valuable resource for current UAV detection and classification research, particularly on ML- and DL-based approaches.

Data publikacji- Marzec 2024 r.

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions contains the following major sections:

  • Wprowadzenie
  • UAV Classification and Categories
  • Conclusions and Discussion

Post Image- The detection and classification mechanism of UAV based on RF signal analysis. (Post Image Credit: Authors)