Date of Award
Master of Science (MS)
Dr. Feng Luo
Dr. Long Cheng
Dr. Nianyi Li
As the field of computer vision continues to advance, the use of autonomous vehicles in military applications has increased and the tasks associated with these systems have grown in scope and complexity. These vehicles tend to operate in combat situations, presenting significant risk in the form of sensor damage. Since these autonomy algorithms rely on sensor information to navigate their environment, any threat to sensor functionality negatively impacts the reliability of the system. As a potential solution, I propose Dynamic Diffusion-based View Translation (DDVT), a novel computer vision algorithm capable of restoring image sensor function in ground vehicles through aerial to ground view translation using images from drones. Previous image to image techniques rely on well-defined and fixed translation parameters, however, the unpredictable nature of drone motion introduces uncertainty into the translation process as the translation distance between aerial and ground sensors is constantly changing. To address this challenge, DDVT leverages a transformer model backbone that introduces novel intermediate cross attention layers and translation label embeddings to facilitate dynamic view translation. DDVT is evaluated using numerous quantitative and visual benchmarks, ultimately confirming its potential for restoring sensor functionality through aerial to ground view translation using drones.
Byrd, Grayson, "Dynamic Translation of Drone View to Vehicle View for Autonomous Driving" (2023). All Theses. 4190.
Author ORCID Identifier
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