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Papers
61,005 resultsShowing papers similar to Neural Network Analysis for Microplastic Segmentation
ClearComputer 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.
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.
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 in Coastal Environments Based on Semantic Segmentation
Researchers developed a deep learning semantic segmentation approach for detecting microplastics on sandy beaches at the pixel level, evaluating 12 models including U-Net variants and transformer architectures under real-world conditions.
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.
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 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.
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.
SMACC: A System for Microplastics Automatic Counting and Classification
Researchers developed an automated computer vision system (SMACC) that uses image analysis to count and classify plastic particles in beach samples, demonstrating that machine learning can substantially reduce the time and effort required for large-scale beach microplastic monitoring.
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.
MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams
Researchers developed MP-Net, a deep learning model based on U-Net architecture, that accurately segments and quantifies fluorescent microplastics in microscopy images of clams, achieving over 90% accuracy and enabling faster, more reliable environmental monitoring.
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.
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.
Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data
Researchers developed an innovative method combining photoacoustic imaging with deep learning to rapidly detect and characterize microplastics. The photoacoustic technology captured high-resolution images of diverse microplastic samples, while the neural network automated the classification process. The study demonstrates that this combined approach could enable faster, more accurate microplastic monitoring compared to conventional methods.
Microplastic Identification Using AI-Driven Image Segmentation and GAN-Generated Ecological Context
Researchers built an AI-powered image segmentation system that can automatically identify microplastics in microscopic photos, then used a generative AI model to create synthetic training images to improve its accuracy. The system reached an F1 score of 0.91, outperforming a model trained without generated data, pointing toward faster and cheaper microplastic identification compared to current expert-driven methods.
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.
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.
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.
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.
Intelligent classification and pollution characteristics analysis of microplastics in urban surface waters using YNet
Researchers developed an AI-based system called YNet to automatically identify and classify microplastics in urban water samples from their visual appearance. The system achieved over 90% accuracy in distinguishing different microplastic shapes and was used to analyze pollution patterns in wetlands and reservoirs. The study demonstrates that artificial intelligence can make microplastic monitoring faster and more consistent compared to traditional manual identification methods.
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.
Microplastic Identification in Seawater using Generative Adversarial Networks
Researchers trained a generative adversarial network (GAN) on microscope images of seawater samples and achieved 92.5% accuracy in automatically distinguishing microplastic particles from natural particulates. This AI-based detection approach could dramatically speed up the analysis of water samples, making routine monitoring of marine microplastic pollution faster and more scalable.