Deep learning-based phase measuring deflectometry for one-shot measurement and inspection of specular free-form surfaces

Manh The Nguyen; Young-Sik Ghim; Hyug-Gyo Rhee

doi:10.1117/12.3025140

30 September 2024 Deep learning-based phase measuring deflectometry for one-shot measurement and inspection of specular free-form surfaces

Manh The Nguyen, Young-Sik Ghim, Hyug-Gyo Rhee

Author Affiliations +

Proceedings Volume 13135, Interferometry and Structured Light 2024; 1313503 (2024) https://doi.org/10.1117/12.3025140
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Deflectometry is a non-contact optical technique used for measuring specular free-form surfaces by projecting structured light using a display screen (LCD screen). While one-shot deflectometry has been extensively researched to develop reliable measurement methods, it is very challenging to measure and inspect complex surfaces using one-shot with low reflectivity because of obtaining phase information from a single, poor-quality complex pattern remains difficult. To address this, we propose a novel single-shot deflectometry approach that utilizes deep learning (DL) to measure complex surfaces accurately in a single shot. We employ DYnet++, a deep learning network model capable of retrieving phase information from single composite patterns. By comparing the results with the 16-step phase-shifting (PS) method, we validate the feasibility and effectiveness of our deep learning-based single-shot deflectometry, which offers potential applications in various industries.

Citation Download Citation

Manh The Nguyen, Young-Sik Ghim, and Hyug-Gyo Rhee "Deep learning-based phase measuring deflectometry for one-shot measurement and inspection of specular free-form surfaces", Proc. SPIE 13135, Interferometry and Structured Light 2024, 1313503 (30 September 2024); https://doi.org/10.1117/12.3025140

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