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Papers
20 resultsShowing papers similar to Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
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.
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.
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.
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.
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-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.
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.
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.
Automated micro-plastic detection and classification using deep convolution neural network pre-trained models and transfer learning
Researchers compared several artificial intelligence models for automatically detecting and classifying microplastics into categories like beads, fibers, and fragments from images. While the models performed well at identifying fiber-type microplastics, they struggled with beads and fragments, highlighting the need for better training data and techniques. Improving automated detection is important because it could enable faster, cheaper environmental monitoring of microplastic contamination in water and food sources.
Deep learning based approach for automated characterization of large marine microplastic particles
A deep learning approach using Mask R-CNN was trained on 3,000 images of marine microplastic particles to automatically locate, classify, and segment particles by shape categories including fiber, fragment, pellet, and rod. The model achieved high accuracy and outperformed manual visual inspection for characterizing large marine microplastic datasets.
Neural Network Analysis for Microplastic Segmentation
Researchers developed a neural network-based image analysis method for automatically detecting and segmenting microplastic particles in photos of beach sand. The approach uses U-Net and MultiResUNet architectures to identify the small particles. Automated image analysis tools like this could significantly speed up the labor-intensive process of counting and characterizing microplastics in environmental samples.
Machine learning enhanced machine vision system for micro-plastics particles classification
Researchers developed a machine learning-based classification system using fluorescence microscopy with Nile Red staining to identify and categorize microplastic types in environmental samples, aiming to provide a faster and more automated alternative to labor-intensive manual identification methods.
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.
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.
Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics
Researchers tested nine deep learning architectures for automating the shape classification of microplastic particles in hyperspectral images, comparing performance on original and augmented datasets. The best models achieved high classification accuracy, offering a faster and more consistent alternative to labour-intensive manual identification.
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.
Instance Segmentation for the Quantification of Microplastic Fiber Images
Researchers applied deep learning instance segmentation to automatically count and measure microplastic fibers in microscope images, replacing tedious manual analysis. The automated method achieved high accuracy and could significantly accelerate microplastic quantification workflows in research and monitoring programs.
Implementing Edge Based Object Detection For Microplastic Debris
This study developed an edge-based object detection algorithm for identifying microplastic debris in images. Automated detection methods are important for scaling up microplastic monitoring, particularly in field settings where manual visual inspection of thousands of particles is impractical.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
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.