Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Joel Samu, Graduate Student
Lead Presenter's Name
Joel Samu
Lead Presenter's College
DB College of Aviation
Faculty Mentor Name
Chuyang Yang
Abstract
The rise of small unmanned aerial systems (sUAS) near airports presents growing safety risks, including mid-air collisions, operational disruptions, and security threats. Current detection systems, such as radar and optical tracking, struggle to reliably identify and classify aerial — particularly non-cooperative drones — under variable operational conditions.
This research proposes a multi-modal aerial object detection system that combines six sensor modalities to enable real-time surveillance. By integrating sensor fusion and machine learning (ML), the system aims to improve detection and classification accuracy, reduce false positives, and support Real-time Decision-making for airport safety personnel. The research aims to evaluate system performance under varied weather and lighting conditions and develop a scalable framework for enhanced Situational Awareness in airport operations.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Multi-Modal Aerial Object Detection for Enhanced Airport Safety
The rise of small unmanned aerial systems (sUAS) near airports presents growing safety risks, including mid-air collisions, operational disruptions, and security threats. Current detection systems, such as radar and optical tracking, struggle to reliably identify and classify aerial — particularly non-cooperative drones — under variable operational conditions.
This research proposes a multi-modal aerial object detection system that combines six sensor modalities to enable real-time surveillance. By integrating sensor fusion and machine learning (ML), the system aims to improve detection and classification accuracy, reduce false positives, and support Real-time Decision-making for airport safety personnel. The research aims to evaluate system performance under varied weather and lighting conditions and develop a scalable framework for enhanced Situational Awareness in airport operations.