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HMPD: A Novel Dataset for Microplastics Classification with Digital Holography
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Researchers developed HMPD, a novel digital holography dataset for training machine learning models to classify microplastic microfibers shed from synthetic textiles during washing. Automated, high-throughput detection methods like this are needed to monitor laundry-derived microfibers — one of the largest contributors to environmental microplastic loads — at scale in wastewater and environmental samples.
Microfiber shedding caused by the washing of synthetic textiles is widely recognized as a major source of environmental microplastic pollution. Such a phenomenon pushed the scientific community to develop ever better methodologies for the detection and the...
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Researchers demonstrated that digital holography can non-invasively detect and reconstruct the three-dimensional shapes of different microplastic types in water, including in the presence of biological matter such as diatoms, offering a promising new optical tool for environmental monitoring.
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Researchers developed a new automated system combining digital holography and polarization imaging with deep learning to rapidly identify and classify microplastics in water, achieving 97.2% accuracy across five plastic types — potentially making environmental monitoring of aquatic microplastic pollution faster and more reliable.
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Deep neural networks were applied to classify microplastic fibers captured via digital holography microscopy, using image fusion techniques on the Holography Micro-Plastic Dataset benchmark. The study demonstrated promising accuracy for distinguishing microplastics from other debris, advancing automated microplastic identification in water quality monitoring.
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Researchers combined laser-induced fluorescence with machine learning to identify microplastics in water in real time, achieving 97.6% accuracy in distinguishing plastics from other organic matter and 88.3% accuracy in identifying specific plastic types — a promising step toward automated ocean monitoring.
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