Combining denoising and object detection

An analysis to provide insights in combining denoising with object detection

Master thesis (2023)

Authors

R.M. Huizer Electrical Engineering, Mathematics and Computer Science

Contributors

O.S. Kayhan Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
N. Yorke-Smith Algorithmics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Rick Huizer
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Published Date

21-08-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Automated imaging systems, critical in domains like medical imaging, autonomous driving, and security, experience noise from camera sensors and electronic circuits in bad or dark lighting conditions. This impacts downstream tasks, including object detection. However, an analysis of strategies combining denoising and object detection is lacking. This study addresses this gap by analyzing diverse strategies for optimizing both image quality and detection performance. Results reveal that isolating denoiser network optimization and training a detector on its outputs yields the best overall performance. Combining detection and denoising enhances detection outcomes. The results offer valuable insights to make educated decisions on how to combine denoising and detection in modern imaging systems.