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Papers
61,005 resultsShowing papers similar to WaveFilter: Advanced Imaging for Marine Microplastic Monitoring
ClearImplementation of YOLOv5 for Detection and Classification of Microplastics and Microorganisms in Marine Environment
Researchers trained a YOLOv5 deep learning model on marine environment images and demonstrated it can accurately detect and classify both microplastics and microorganisms in real time, offering a memory-efficient tool for automated environmental monitoring.
TECI-YOLO: An Efficient, Lightweight Model for Detecting Small Floating Objects on Water Surfaces
Despite its title referencing floating object detection on water, this paper studies a machine learning model (TECI-YOLO) for detecting small objects on water surfaces using computer vision — not microplastic pollution. It examines improvements in detection accuracy and computational efficiency for real-time marine monitoring and is not directly relevant to microplastics research.
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.
YOLOv7-Based Microplastic Detection: Crafting a Custom Dataset for Environmental Analysis
Researchers used three versions of the YOLO object detection model to detect and count microplastics from a custom-built dataset. YOLOv8 achieved the highest overall accuracy at 81.4%, followed by YOLOv7 at 80.7% and YOLOv9 at 77.2%, though YOLOv7 performed best with real-time test data. The study demonstrates the potential of AI-based detection systems for automating microplastic identification in environmental samples.
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.
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.
IoT-Integrated Image Recognition System for Microplastic Detection and Classification
Researchers developed an IoT-based system that combines a microscopic camera with a YOLOv8 deep learning model to detect and classify microplastics in real time, including types like LDPE, PE, PHA, and PS. The system achieves high accuracy across diverse environmental conditions and visualizes data through a cloud-based dashboard. This scalable approach offers a practical tool for monitoring microplastic pollution, with potential for future integration on marine vessels.
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.
YOLOv8-C2f-Faster-EMA: An Improved Underwater Trash Detection Model Based on YOLOv8
Researchers improved an AI-based object detection system (YOLOv8) to better identify small pieces of underwater trash, achieving a 5% improvement in detection accuracy. Automated trash detection in waterways matters because removing plastic waste before it breaks into microplastics can reduce the amount of tiny plastic particles that eventually contaminate drinking water and seafood.
AI – Driven Marine Debris Detection for Ocean Conservation
Researchers developed an AI-driven marine debris detection system using the YOLOv8 deep learning model trained to identify plastic waste in challenging underwater conditions including low visibility and complex backgrounds. The system aims to provide scalable, automated monitoring to support ocean conservation and guide debris removal efforts.
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.
Underwater Waste Recognition and Localization Based on Improved YOLOv5
Researchers developed an improved YOLOv5-based algorithm incorporating weighted image fusion to enhance detection and localization of underwater plastic waste in optical images, addressing challenges of noise, low contrast, and blurred textures in aquatic environments.
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.
High-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.
Identification and detection of microplastic particles in marine environment by using improved faster R–CNN model
Researchers developed an improved Faster R-CNN deep learning model for identifying and detecting microplastic particles in marine environments. The model achieved an average detection confidence of 99% and successfully distinguished polystyrene microplastics from mixed particle suspensions across varying backgrounds and conditions, demonstrating a promising automated approach for monitoring microplastic pollution.
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.
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.
Detecting Microplastics in Seawater with a Novel Optical Sensor Based on Artificial Intelligence Models
Detecting microplastics in seawater quickly and accurately is a major technical challenge, and this study developed a novel optical sensor that uses artificial intelligence to identify plastic particles from light-scattering data in real time. The AI-powered system was tested on seawater samples and showed promising accuracy for classifying microplastic types without the need for time-consuming laboratory processing. Automated in-situ sensors like this could enable continuous, large-scale ocean monitoring for microplastic pollution.
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.
Slim Deep Learning Approach for Microplastics Image Classification in the Marine Environment
Researchers developed a lightweight convolutional neural network called the Slim-DL-Model for classifying microplastics in marine environment images, designed to overcome the computational demands of existing architectures like VGG16 and ResNet for real-time field applications. The model achieves competitive classification accuracy while significantly reducing computational requirements, enabling deployable microplastic monitoring systems.
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.
Smart Ocean Cleanup: An AI-Integrated Autonomous System for Marine Waste Management
This paper presents an AI-powered autonomous boat system designed to detect and collect marine pollution — including plastics, oil spills, and microplastics — using deep learning image classification, IoT sensors, and robotic collection mechanisms. The system demonstrated over 94% accuracy for pollutant detection and classification across several AI models. While focused more broadly on ocean cleanup technology than on microplastic science specifically, it demonstrates how AI-integrated robotics could help address the practical challenge of removing plastic waste from ocean surfaces before it breaks down further.
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.
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.