The observation and detection of the microplastic pollutants generated by industrial manufacturing require the use of precise optical systems. Digital holography is well suited for this task because of its non-contact and non-invasive detection features and the ability to generate information-rich holograms. However, traditional digital holography usually requires post-processing steps, which is time-consuming and may not achieve the final object detection performance. In this work, we develop a deep learning-based holographic classification method, which computes directly on the raw holographic data to extract quantitative information of the microplastic pollutants so as to classify them according to the extent of the pollution. We further show that our method can generalize to the classification task of other micro-objects through cross-dataset validation. Without bulky optical devices, our system can be further developed into a portable microplastics detection system, with wide applicability in the monitoring of microplastic particle pollution in the ecological environment.

工业制造产生的微塑料污染物的观察和检测需要使用精密的光学系统。数字全息术非常适合这项任务，因为它具有非接触式和非侵入性检测功能，并且能够生成信息丰富的全息图。然而，传统的数字全息通常需要后期处理步骤，耗时且可能无法达到最终的物体检测性能。在这项工作中，我们开发了一种基于深度学习的全息分类方法，该方法直接对原始全息数据进行计算，提取微塑料污染物的定量信息，从而根据污染程度对它们进行分类。我们进一步表明，我们的方法可以通过跨数据集验证推广到其他微对象的分类任务。无需庞大的光学器件，我们的系统可以进一步发展成为便携式微塑料检测系统，在生态环境中微塑料颗粒污染的监测中具有广泛的适用性。