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

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.