Date of Award
2022
Degree Type
Thesis
Degree Name
Master of Science in Electrical Engineering (MSEE)
Department
Electrical Engineering
First Advisor
Resit Sendag
Abstract
Three alterations are investigated: The addition of a feature pyramid network to improve small object recognition, a loss weighting strategy dependent on object poses, and the addition of a transformer for feature comparisons.As a result increased performance was achieved for both the FPN and loss weighting alterations while the transformer alterations slightly underperformed the original model.
Creative Commons License
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Recommended Citation
Frink, Travis, "ENHANCED DEEP TEMPLATE-BASED OBJECT INSTANCE DETECTION" (2022). Open Access Master's Theses. Paper 2238.
https://digitalcommons.uri.edu/theses/2238