Optical characteristics optimized for machine perception using learning-based losses backpropagation through optical simulation pipeline

J. Buquet; R. Larouche; J. Parent; P. Roulet; S. Thibault

doi:10.1117/12.2633850

3 October 2022 Optical characteristics optimized for machine perception using learning-based losses backpropagation through optical simulation pipeline

J. Buquet, R. Larouche, J. Parent, P. Roulet, S. Thibault

Author Affiliations +

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270B (2022) https://doi.org/10.1117/12.2633850
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

As more and more cameras are used for machine perception, the optical design process still relies on key indicators such as point spread function (PSF), modulated transfer unction (MTF) based on aberration minimization. This process has proven efficient for human vision but is not tailored for machine perception. Given a specific computer vision task, it is not always necessary to target the same key performance indicators (KPIs) than when images are visualized by humans. Moreover, this image quality might change during a camera lifespan with the appearance of defocus for example. It is crucial to be able to determine how this kind of degradation can affect a computer vision task. In this work we study the impact of defocus on 2D object identification and show that, for a certain design, it is not impacted by image degradation under a certain threshold. We also demonstrate that this threshold is higher for lower f-number which makes them better design candidates.

Conference Presentation

Citation Download Citation

J. Buquet, R. Larouche, J. Parent, P. Roulet, and S. Thibault "Optical characteristics optimized for machine perception using learning-based losses backpropagation through optical simulation pipeline", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270B (3 October 2022); https://doi.org/10.1117/12.2633850

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