We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
20 resultsShowing papers similar to Image recognition of microplastic particles in marine sediments – planned activities
ClearAutomatic 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.
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.
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.
Microplastic and nanoplastic analysis methods, tests and reference materials
Researchers described a workflow combining a streamlined experimental setup with automated image analysis to quantify marine microplastic debris, addressing the limitations of labor-intensive manual counting methods that currently prevent scalable and consistent global plastic monitoring.
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.
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.
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.
Proposal for an initial screening method for identifying microplastics in marine sediments
Researchers developed a simplified screening protocol to identify microplastics in marine sediment samples, intended as a rapid initial method before more detailed analysis. Standardized screening protocols that are accessible to more laboratories are needed to expand global monitoring of microplastic sediment contamination.
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.
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.
Implementation of an open source algorithm for particle recognition and morphological characterisation for microplastic analysis by means of Raman microspectroscopy
An automated particle recognition algorithm was implemented to speed up the identification and measurement of microplastics in Raman spectroscopy images. Automated analysis reduces the time and subjectivity involved in manual microplastic counting, improving research efficiency.
Automated method for routine microplastic detection and quantification
An automated image processing algorithm was developed and validated for quantifying microplastics on filter membranes, comparing results with expert visual counting. The semi-automatic method performed comparably to experienced operators on samples from cave sediment and river water, reducing analysis time substantially.
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.
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.
Identification of Microplastics in Aquatic Environments Using Oxidative Treatment and Automated Image Analysis
Researchers developed a cost-effective and replicable method for detecting microplastics in freshwater environments using oxidative treatment to digest organic matter from water samples, enabling cleaner isolation and more accurate identification of MP particles without requiring expensive instrumentation.
A Simplified Experimental Method to Estimate the Transport of Non-Buoyant Plastic Particles Due to Waves by 2D Image Processing
Not a microplastics paper in the strict sense — this study develops and validates an image-processing method to track the movement of non-buoyant plastic debris particles under wave action in a laboratory wave tank, advancing the physical modeling tools used to predict where plastic pollution accumulates in coastal environments.
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.
Identification Tools of Microplastics from Surface Water Integrating Digital Image Processing and Statistical Techniques
This study demonstrated that digital image analysis can automate and improve the characterization of microplastic particles collected from river water, capturing detailed shape, color, and size data that manual microscopy cannot easily achieve at scale. Better identification tools like this are essential for standardizing microplastic monitoring across different waterways and research groups.
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
Researchers developed a deep learning-based coastal debris detection system using YOLOv7 and the SAHI vision library to identify microplastics in image data collected from shorelines. The system demonstrated effective detection performance and offers a scalable approach for automated monitoring of microplastic litter in coastal environments.