Date Approved


Embargo Period


Document Type


Degree Name

M.S. Electrical and Computer Engineering


Electrical and Computer Engineering


Henry M. Rowan College of Engineering


Ramachandran, Ravi P.

Committee Member 1

Bouaynaya, Nidhal C.

Committee Member 2

Rasool, Ghulam


Artificial Intelligence, Computer Vision, Deep Learning, Rotorcraft, Safety


Helicopters--Accidents; Computer vision


Aviation | Electrical and Computer Engineering


The recent impact of deep learning algorithms and their major breakthroughs on various aspects of our lives has led to the idea to investigate the application of these algorithms in different problem spaces. One of the novel areas of investigation is the aviation and air traffic control domain; as it offers a prime opportunity to enhance safety within the aviation community. Of particular importance to this community is improving the safety of rotorcraft operations, as this segment of the aviation industry is subject to a higher fatal accident rate than other segments of the industry. The improvement of safety for rotorcraft also directly improves the safety and efficiency of air traffic control, since rotorcraft operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants such as unmanned aircraft systems, urban air mobility, etc.

The novel method for improving rotorcraft safety, and the main topic of this research, is to create an algorithm that determines the head position of helicopter pilots and copilots through automatic post-processing of onboard flight video data. This information can then be used to aid in incident/crash analysis as well as future vision systems research. Both a classical computer vision technique and a deep learning approach were taken to provide possible solutions to this problem. Both solutions successfully deal with the issues of excessive cockpit background, extreme head positions, and added noise from the pilot's operational equipment which include helmets, microphones, and sunglasses.