Digital holography with deep learning and generative adversarial networks for automatic microplastics classification

Yanmin Zhu; Chok Hang Yeung; Edmund Y. Lam

doi:10.1117/12.2575115

10 October 2020 Digital holography with deep learning and generative adversarial networks for automatic microplastics classification

Yanmin Zhu, Chok Hang Yeung, Edmund Y. Lam

Author Affiliations +

Proceedings Volume 11551, Holography, Diffractive Optics, and Applications X; 115510A (2020) https://doi.org/10.1117/12.2575115
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

Microplastics, which are a major source of pollution in the ocean, need to be accurately detected and monitored. However, the current detection approaches often require complex optical instrumentation and a long time for image processing. Furthermore, because of the difficulties of particle sampling, it is hard to collect a dataset with sufficient images and a balanced distribution. Digital holography, which is a non-destructive imaging method, is suitable for the in situ imaging. In this work, we propose a novel digital holography microplastics classification system which combines deep learning and generative adversarial networks. We experimentally show that our method yields a higher accuracy for microplastics classification and can efficiently reduce the imbalance ratio of the dataset. This method can be modified for other in situ image classification tasks that likewise suffer from a small and imbalanced distribution dataset.

Conference Presentation

Citation Download Citation

Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam "Digital holography with deep learning and generative adversarial networks for automatic microplastics classification", Proc. SPIE 11551, Holography, Diffractive Optics, and Applications X, 115510A (10 October 2020); https://doi.org/10.1117/12.2575115

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