Enhancing Vehicle Detection in Low-Light Imagery Using Polarimetric Data
Date of Award
12-12-2024
Degree Name
M.S. in Electrical Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Bradley Ratliff
Abstract
RGB imagery provides detail which is usually sufficient to perform computer vision tasks. However, images taken in low-light appear vastly different from well-lit imagery due to the diversity in light intensity. Polarimetric data provides additional detail which focuses on the orientation of the light rather than intensity. Scaling our classic RGB images using polarimetric data can maintain the RGB image type, while also enhancing image contrast. This allows transfer learning using pre-trained RGB models to appear more feasible. Our work focuses on developing a large dataset of paired polarimetric RGB images in a highly controlled laboratory environment. Then, we perform transfer learning on a pre-trained image segmentation model with each of our image product types. Finally, we compare these results in both well-lit and low-light scenarios to see how our polarimetrically enhanced RGB images stack up against regular RGB images.
Keywords
Polarization, Object Detection, Image Enhancement, Image Segmentation, Stokes Images, Image Scaling, Low-Light Imagery, Remote Sensing, Data Collection
Rights Statement
Copyright © 2024, author.
Recommended Citation
Schafer, Austin, "Enhancing Vehicle Detection in Low-Light Imagery Using Polarimetric Data" (2024). Graduate Theses and Dissertations. 7485.
https://ecommons.udayton.edu/graduate_theses/7485
