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Papers
20 resultsShowing papers similar to Efficient Microplastic Detection in Water Using ResNet50 and Fluorescence Imaging
ClearDeep 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.
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.
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.
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.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Deep Classification of Microplastics Through Image Fusion Techniques
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.
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.
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.
GoogLeNet-Based Deep Learning Framework for Underwater Microplastic Classification in Marine Environments
Researchers trained a GoogLeNet deep learning model on underwater images to classify microplastics into four categories, achieving strong classification performance for primary microplastics, secondary microplastics, non-microplastic debris, and marine biota in turbid coastal waters.
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.
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.
Application of a convolutional neural network for automated multiclass identification of field-collected microplastics and diatom algae from optical microscopy images
Researchers developed and evaluated a convolutional neural network model using transfer learning to automatically classify field-collected microplastics and diatom algae from optical microscopy images, using a dataset of real microplastics sampled from a freshwater reservoir. The model achieved automated multi-class identification, including detection of diatom frustules that survive hydrogen peroxide processing, addressing challenges posed by the lack of standardised microplastic analysis protocols.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
Microplastic pollution monitoring with holographic classification and deep learning
This study used digital holographic microscopy combined with deep learning to classify microplastic particles in water samples, achieving high classification accuracy and demonstrating the potential for automated, high-throughput microplastic monitoring.
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
Researchers created a new open-source dataset of microscopy images for training AI models to automatically detect and classify micro- and nanoplastics. The dataset fills an important gap in available tools for microplastic research and provides a foundation for developing faster, more efficient methods to identify plastic contamination across environmental samples.
Identification of marine microplastics by laser-induced fluorescence spectroscopy: 1-Dimensional convolutional neural network and continuous convolutional model
Researchers investigated using laser-induced fluorescence spectroscopy combined with deep learning models to identify six types of marine microplastics. A continuous convolution neural network model achieved 99.5% classification accuracy, outperforming a standard 1D convolutional network at 97.5%. The approach offers a faster and less expensive alternative to traditional FTIR and Raman spectroscopy methods for microplastic identification.
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.
Detection of Microplastic Ingestion in the Human Body Using Deep Learning Technique
Researchers applied convolutional neural networks trained in MATLAB to detect and quantify microplastic contamination in high-resolution tissue images, demonstrating that deep learning can automate the identification of plastic particles in biological 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.
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.