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Papers
20 resultsShowing papers similar to Implementing Edge Based Object Detection For Microplastic Debris
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.
Image recognition of microplastic particles in marine sediments – planned activities
This abstract outlines a planned research effort to develop image recognition algorithms for automatically identifying microplastic particles in marine sediment samples. Automated identification could greatly speed up the labor-intensive task of quantifying microplastics in sediment, enabling broader and more consistent environmental monitoring.
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.
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.
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.
Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment
This paper is not directly about microplastic pollution. It presents an improved computer vision system (YOLOv5s) for detecting floating objects on water surfaces using edge computing. While such technology could potentially be applied to detect plastic debris in rivers, the study itself focuses on the object detection algorithm rather than microplastics specifically.
Detection of Secondary Microplastics in an Aquatic Mesocosm by Means of Object-Based Image Analysis
Researchers evaluated object-based image analysis for detecting secondary microplastics of polypropylene, polyethylene terephthalate, and low-density polyethylene suspended in an aquatic mesocosm under both still and turbulent conditions. The imaging approach successfully identified microplastics in both conditions, supporting its development as a monitoring tool for plastic particles in water.
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.
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.
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 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.
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.
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.
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.
Image processing tools in the study of environmental contamination by microplastics: reliability and perspectives
Researchers assessed the reliability of image processing tools for studying microplastic contamination, finding that while these tools offer efficiency gains, inconsistent methodologies limit comparability between studies and call for standardization.
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.
Deep-Feature-Based Approach to Marine Debris Classification
This study applied deep learning to classify marine debris from images, demonstrating that feature-based neural network approaches can effectively distinguish plastic types and other debris categories to support automated ocean monitoring.
On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring
This review evaluates the current state of satellite and aerial remote sensing for detecting marine plastic debris, noting that while progress has been made using optical and hyperspectral imaging, significant challenges remain including low detection resolution for small particles, confusion with other floating materials, and the need for better machine learning algorithms. The paper is relevant to the microplastics field as large-scale monitoring tools are needed to track plastic pollution distribution and inform cleanup and policy efforts, though direct detection of microplastics (<5 mm) from orbit remains largely out of reach with current technology.
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.