Date Approved

1-27-2026

Embargo Period

1-27-2026

Document Type

Thesis

Degree Name

M.S. Electrical and Computer Engineering

Department

Electrical and Computer Engineering

College

Henry M. Rowan College of Engineering

Advisor

Nidhal C. Bouaynaya, Ph.D.

Committee Member 1

Ying Tang, Ph.D.

Committee Member 2

Charles C. Johnson

Keywords

AI;Aviation safety;Deep Learning;Machine Learning

Abstract

The growing complexity of aviation operations, particularly in rotorcraft and vertical flight necessitates the development of intelligent, automated systems to enhance safety and situational awareness. This thesis investigates the application of artificial intelligence (AI), with a focus on deep learning (DL), to address critical safety challenges in aviation. Four core use cases are examined: helicopter cockpit flight data monitoring, runway detection, helipad segmentation, and obstacle localization around rotorcraft landing zones. Each application targets a specific gap in current aviation infrastructure, with an emphasis on operational needs identified by the Federal Aviation Administration (FAA). Leveraging a range of DL architectures, including convolutional neural networks and zero-shot vision-language models, this work demonstrates how modern DL computer vision techniques can be effectively applied to complex, safety-critical tasks using video footage and high-resolution satellite imagery. The proposed models are designed for scalability and seamless integration into existing aviation workflows, ensuring both practicality and impact. The results underscore DL’s potential to modernize aviation safety systems, enhance situational awareness, and enable more proactive and data-driven safety management practices.

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