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Microplastic pollution monitoring with holographic classification and deep learning
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This study used digital holographic microscopy combined with deep learning to classify microplastic particles in water samples, achieving high classification accuracy and demonstrating the potential for automated, high-throughput microplastic monitoring.
Abstract 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.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.