Optical characteristics optimized for machine perception using learning-based losses backpropagation through optical simulation pipeline
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.
- Publication:
-
Applications of Machine Learning 2022
- Pub Date:
- October 2022
- DOI:
- 10.1117/12.2633850
- Bibcode:
- 2022SPIE12227E..0BB