ENHANCED DEEP TEMPLATE-BASED OBJECT INSTANCE DETECTION

Author

Travis Frink, University of Rhode IslandFollow

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

This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

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