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Automatic microfibers detection in SEM images
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This study developed an automated deep learning method to detect and count microfibers in scanning electron microscope (SEM) images, addressing the labor-intensive nature of manual microfiber analysis. The system was validated against manual counting and achieved robust performance, enabling faster processing of SEM datasets for environmental monitoring.
Microplastics, plastic debris with particles size ranging from 1 nm to 5 mm, have a negative health impacts on marine life, resulting in environmental and human health threats. The fiber microplastics are considered one of the major sources of plastic pollution in oceans, probably due to the fact that, because of their shape, they can partially pass-through wastewater treatment plants and reach directly the sea. Microfibers (MFs) pollution come from a variety of sources such as synthetic fishing nets and ropes, but mainly from textiles and their wastewater. MFs produced by washing processes of synthetic textiles is mainly due to the mechanical and chemical stresses due to washing cycles, detergents, temperature and treatment washing time. The existing analytical methods for microplastic quantification range from visual identification to molecular spectroscopy. In the microscopy field, the use of a Scanning Electron Microscope (SEM) offers extremely clear and high-resolution images of plastic particles, but related image analysis has proven more challenging than in traditional optical microscopy. Unaided visual identification and count of microfibers require skilled and experienced operators and results to be a lowthroughput and subjective method. The development and application of automated techniques for extracting relevant picture features and support the final human counting has therefore become necessary. A large AI model, applied for fibers detection and counting in SEM images, is presented here. It is based on Zero-shot learning, an approach that leverages the deductive capabilities of models, making it possible to be applied, for several tasks of fibers counting in SEM images, without human intervention. The proposed approach is tested and validated on a set of micrographs of wastewater from four washing cycles of cotton fabrics.
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Researchers developed a deep learning system that can automatically detect and classify microplastics in scanning electron microscope images, replacing the time-consuming process of manual analysis. The system achieved high accuracy in identifying different types and shapes of microplastic particles, even very small ones that are difficult to spot by eye. This automated approach could significantly speed up microplastic monitoring and pollution assessment efforts.
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This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
A Deep Learning Approach for Microplastic Segmentation in Microscopic Images
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Researchers developed a deep learning model for automated segmentation and classification of microplastics in microscopic images, identifying five distinct categories including fibers, fragments, spheres, foam, and film. The model achieved high accuracy while maintaining low computational requirements, making it suitable for high-throughput deployment in environmental monitoring. The study offers a tool that could help overcome the measurement bottleneck in microplastic characterization for toxicological and risk assessment studies.
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