Abstract
The increasing proliferation and accessibility of radioactive sources necessitates rapid, safe, and efficient methods of radiation detection without direct exposure. Unmanned aerial vehicles (UAVs) have become potential instruments for radiation detection due to their versatility and mobility. Passive and active detection methods are commonly used, with passive detection measuring natural radiation directly. Environmental factors like wind speed and direction are considered in system design to ensure accuracy and reliability. New techniques and approaches, such as spectroscopic analysis, machine learning, and hybridization, have been developed for optimal-level radiation detection with UAV-based systems. This study provides a state-of-the-art review of UAV-based radiation detection systems, focusing on the type of radiation systems, detection techniques, methodologies, application areas, and limitations. Proposed ways to improve performance and accuracy through further research studies are highlighted. Potential applications of UAV-based radiation detection systems include disaster response, environmental monitoring, and nuclear facility inspection. This comprehensive overview provides a valuable resource for researchers and practitioners in the field.
| Original language | English |
|---|---|
| Title of host publication | 1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350348637 |
| DOIs | |
| State | Published - 2024 |
| Event | 1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024 - Muscat, Oman Duration: 14 May 2024 → 15 May 2024 |
Publication series
| Name | 1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024 - Proceedings |
|---|
Conference
| Conference | 1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024 |
|---|---|
| Country/Territory | Oman |
| City | Muscat |
| Period | 14/05/24 → 15/05/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Machine learning
- Radiation detection
- Spectroscopic analysis
- UAV
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Aerospace Engineering
- Civil and Structural Engineering
- Safety, Risk, Reliability and Quality
- Computational Mathematics
- Control and Optimization
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