Thesis Defense Announcement for Haley Honigfort – 3/18/25 at 9 AM
March 3, 2025
Thesis Title: Semantic Segmentation via Transfer Learning for Off-Road Data
When: March 18, 2025 (09:00 AM Local)
Where: Simrall 228 and Microsoft Teams (Meeting Link)
Candidate: Haley Honigfort
Degree: Master of Science in Electrical & Computer Engineering
Committee Members: Dr. John Ball, Dr. Lalitha Dabbiru, Dr. Chun-Hung Liu, and Dr. Stanton Price
Abstract
In autonomous driving, utilizing deep learning models to help make decisions has become a popular theme, particularly in the realm of computer vision. Deep learning models are heavily influenced to make decisions based on the environments in which they are trained. Currently, very few datasets exist for off-road autonomy that include visual semantic segmentation labels. Traditional semantic segmentation requires hand-labeling large imagery datasets or using synthetically generated imagery to train a model. This study aims to apply transfer learning techniques to automatically label a new off-road dataset. That objective will be accomplished in two phases: first, by utilizing pre-existing labels of published off-road datasets, and second, by creating new labels derived from a well-performing model and a small subset of hand-labeled imagery. Additionally, this thesis investigates a proposed advancement to the auto-labeling pipeline using features extracted from auto-encoders.