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Papers
20 resultsShowing papers similar to Instance Segmentation for the Quantification of Microplastic Fiber Images
ClearA Deep Learning Approach for Microplastic Segmentation in Microscopic Images
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.
Development of Microplastics Detector and Quantifier Utilizing Deep Learning Based Algorithm
Researchers developed a microplastics detector and quantifier using deep learning-based image analysis, training a neural network to identify and count microplastic particles in microscopic images. The system achieved high accuracy and offers a faster, more objective alternative to manual counting.
Computer vision segmentation model—deep learning for categorizing microplastic debris
Researchers developed a deep learning computer vision model for automatically categorizing beached microplastic debris from images. The segmentation model was trained to identify and classify different types of microplastic particles, reducing the need for time-consuming manual counting and laboratory analysis. The study suggests that automated image-based detection could enable more scalable and consistent monitoring of microplastic pollution along coastlines.
Proceeding the categorization of microplastics through deep learning-based image segmentation
Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
Rapid Classification of Microplastics by Using the Application of a Convolutional Neural Network
Researchers used convolutional neural networks (deep learning) to automatically classify microplastic particles in microscopy images into four categories: fragments, pellets, films, and fibers. The models achieved high classification accuracy, reducing the time and labor needed for manual identification. Automated AI classification could greatly accelerate large-scale microplastic monitoring programs.
Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water
Researchers developed a rapid analysis system for quantifying microplastic fibers in water using a high-resolution lensless shadow microscope combined with deep learning algorithms. The approach replaces the slow manual counting process with automated imaging on a chip, significantly increasing both speed and accuracy. The study offers a practical tool for routine monitoring of microplastic fiber pollution in water treatment and environmental settings.
Deep learning approach for automatic microplastics counting and classification
Researchers developed a deep learning architecture combining U-Net segmentation and VGG16 classification to automatically count and categorise microplastic particles of 1-5 mm into fragments, pellets, and lines from digital camera images. The system reduces the cost and time of traditional microplastic quantification methods while enabling high-throughput monitoring.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Deep learning-powered efficient characterization and quantification of microplastics
Researchers developed an artificial intelligence framework that uses deep learning to automatically identify and quantify microplastics from infrared spectra and visual images. The system achieved high accuracy in classifying plastic types and counting particles, dramatically reducing the time needed compared to manual analysis. This tool could make large-scale microplastic monitoring faster and more consistent across different research laboratories.
Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning
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.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
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.
Automatic microfibers detection in SEM images
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.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
Big Data, Tiny Targets: An Exploratory Study in Machine Learning-enhanced Detection of Microplastic from Filters
Researchers applied machine learning algorithms to microscopy images of microplastics on filter papers, demonstrating that AI-assisted automated detection significantly reduces the manual analysis time required for high-throughput microplastic screening.
Automatic Counting and Classification of Microplastic Particles
Researchers developed an automatic system for counting and classifying microplastic particles in marine samples, applying image analysis techniques to address the growing problem of plastic debris entering the food chain via marine species ingestion.
Efficient Microplastic Detection in Water Using ResNet50 and Fluorescence Imaging
Researchers applied a ResNet50 deep learning model to fluorescence microscopy images of water samples, achieving high-accuracy classification of microplastics, demonstrating that deep learning can efficiently automate microplastic identification from microscopy data.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics
An open-source computer vision application was developed to automatically count and classify microplastics in microscopy images, achieving accuracy comparable to manual counting while processing samples orders of magnitude faster, offering the scientific community a free tool to reduce the bottleneck of tedious visual microplastic enumeration.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks
Researchers developed convolutional neural network models for efficiently identifying and segmenting microplastics in urban water samples from southern China. The study found that deep learning approaches can significantly reduce the time and labor required for microplastic identification compared to manual methods, offering a scalable tool for monitoring microplastic pollution in urban waterways.