Theses and Dissertations
Towards clearer paths: Addressing camera obstructions in autonomous vehicles through neural networks
ORCID
https://orcid.org/0009-0007-9044-747X
Advisor
Swan, J. Edward
Committee Member
Carruth, Daniel
Committee Member
Bethel, Cindy
Date of Degree
5-10-2024
Original embargo terms
Immediate Worldwide Access
Document Type
Graduate Thesis - Open Access
Major
Computer Science (Research Computer Science)
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Computer Science and Engineering
Abstract
This study addresses the challenge of lens obfuscations in off-road autonomous vehicles, which compromise the essential visual inputs for safe navigation. Using a tiered approach, the research employs neural network architectures for preliminary image classification, semantic segmentation, and image-to-image translation to rectify obscured visual inputs. Initial classification using MobileNetV2 sets the stage for U-Net-driven semantic segmentation to identify obfuscated regions, followed by a modified Pix-to-Pix model for image restoration. The evaluation showcases promising results in improving visual clarity, marking a significant stride towards enhancing autonomous vehicle operational robustness in off-road environments. This work lays a foundation for future explorations into advanced neural network architectures for real-time implementations in off-road terrains.
Recommended Citation
Harvel, Nicholas J., "Towards clearer paths: Addressing camera obstructions in autonomous vehicles through neural networks" (2024). Theses and Dissertations. 6117.
https://scholarsjunction.msstate.edu/td/6117