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Papers
20 resultsShowing papers similar to Detection of Secondary Microplastics in an Aquatic Mesocosm by Means of Object-Based Image Analysis
ClearImplementing 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 field deployable imaging system for detecting microplastics in the aquatic environment
Researchers built a portable imaging system for detecting microplastics in water that can be deployed directly in the field rather than requiring laboratory analysis. The system uses a de-scattering algorithm to produce clear images even in turbid water conditions and can identify particles as small as 50 micrometers. This low-cost tool could make routine microplastic monitoring of rivers, lakes, and coastal waters much more practical and accessible.
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.
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.
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.
Detection of floating objects in liquids
Researchers reviewed non-invasive optical and imaging technologies for detecting and characterizing floating particles including microplastics in liquids, motivated by growing concern over microplastic contamination in drinking water and food products. They found that advances in computational imaging and spectroscopic methods offer promising pathways for scalable, real-time monitoring of large water volumes.
Optical System for In-situ Detection of Microplastics
Researchers developed a portable optical system capable of detecting, identifying, continuously monitoring, and quantifying microplastics in situ at natural water bodies. The system uses optical techniques to observe the temporal behavior of microplastic concentrations at fixed locations, enabling real-time environmental monitoring without sample collection and laboratory processing.
Development of a Near-Infrared Imaging System for Identifying Microplastics in Water
Researchers developed a near-infrared imaging system capable of automatically identifying and characterizing microplastics suspended in water, successfully obtaining material identification images without the manual sorting typically required by conventional methods.
Computational polarized holography for automatic monitoring of microplastics in scattering aquatic environments
Researchers developed an integrated imaging system based on computational polarized holography for automatic monitoring of microplastics in aquatic environments. The system enables accurate 3D tracking of dynamic microplastic particles, and a hybrid de-scattering algorithm substantially improves image quality even in turbid water conditions. An unsupervised clustering method was also developed to identify and classify different microplastics based on their multimodal features without manual annotation.
Detection of Microplastic Waste by Using a Novel Microfluidic System with an Integrated Object Tracking Algorithm
Researchers developed a novel microfluidic system integrated with an object tracking algorithm to detect and distinguish microplastics from other materials in water, using multiple microchannel designs fabricated from PDMS microchips. The system demonstrated the ability to observe microplastic flow and deformation behaviour within microchannels, providing a new platform for automated microplastic detection and characterization.
Digital Image Analysis and Multivariate Data Analysis as Tools for the Identification of Microplastics in Surface Waters: The Case of the Vistula River (Central Europe)
Researchers demonstrated digital image analysis combined with microscopy as a tool for identifying and characterizing microplastic particles from Vistula River surface water samples, performing exhaustive quantitative and qualitative evaluation of 2D and 3D morphology to characterize MP abundance and composition.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
Image processing techniques for measuring primary microplastic abundance in various of dispersant
Researchers developed an image processing technique (IPT) using ImageJ software combined with various dispersants to quantify primary microplastic (microbead) abundance directly in liquid samples without prior extraction. They found a strong correlation (R2 > 0.75) between sample mass and particle count and achieved limits of detection of 1.75 particles for polypropylene and 0.00009 for polyethylene, offering a lower-cost alternative to conventional microscopy for microplastic quantification.
Sorting microplastics from other materials in water samples by ultra-high-definition imaging
Researchers used a commercial particle analyzer with ultra-high-definition imaging to sort and identify microplastic particles in water samples. The device successfully distinguished between different plastic types based on how light scatters through or off their surfaces, and could separate microplastics from air bubbles and other non-plastic particles. The study demonstrates a relatively fast and accessible method for characterizing microplastic contamination in water.
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.
Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images
Researchers developed a machine learning system that uses holographic 3D images to automatically classify microplastics in water samples, distinguishing them from other microscopic particles with high precision. Current microplastic monitoring is slow and labor-intensive, so automated detection tools are essential for large-scale environmental surveillance. This approach could significantly speed up the monitoring of microplastic pollution in aquatic environments.
On the use of machine learning for microplastic identification from holographic phase-contrast signatures
This study applied machine learning to identify microplastic types from holographic phase-contrast imaging signatures, achieving rapid automated classification. Automated identification tools are important for scaling up microplastic monitoring in marine waters where manual identification is too slow and labor-intensive.
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.
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.
Enhanced classification of microplastic polymers (polyethylene, polystyrene, low‐density polyethylene, polyhydroxyalkanoate) in waterbodies
Researchers developed a new deep learning model to automatically detect and classify different types of microplastic polymers in water from holographic images. The system combines advanced image segmentation with a vision transformer to identify polyethylene, polystyrene, low-density polyethylene, and polyhydroxyalkanoate particles. The approach aims to improve the speed and accuracy of microplastic monitoring in aquatic environments compared to traditional manual methods.