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Papers
61,005 resultsShowing papers similar to FindingPlastic: Underwater Plastic Bag Detection and Retrieval
ClearAI – 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.
Real-Time Detection of Microplastics Using an AI Camera
Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
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.
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.
Design and Development of Smart Beach Debris Collection and Segregation System
Researchers designed and built a smart automated system for collecting and segregating beach debris, using sensors and robotics to identify and sort plastic waste from natural material on shorelines. The system demonstrated effective separation of plastic debris in field tests.
Development of Drifting Debris Detection System using Deep Learning on Coastal Cleanup
Researchers developed a deep learning-based system to detect litter on beaches using images and automated object recognition. Efficient litter detection tools could help coastal cleanup programs identify and remove plastic debris before it breaks down into microplastics.
Advancing microplastic pollution management in aquatic environments through artificial intelligence
This review examines how artificial intelligence and robotics are being applied to tackle microplastic pollution in aquatic environments, covering waste collection, particle identification, and degradation monitoring. Researchers highlight several successful AI-driven projects deployed by countries and organizations around the world. The study suggests that integrating AI with traditional environmental methods holds significant promise for improving both the speed and accuracy of microplastic management.
“WAVECLEAN” – An Innovation in Autonomous Vessel Driving Using Object Tracking and Collection of Floating Debris
Researchers designed an autonomous vessel called WAVECLEAN that uses object-tracking technology to identify and collect floating marine debris, including plastics. The system combines camera-based detection with machine learning to navigate waterways and gather waste without human operation. The study demonstrates a technology-driven approach to addressing plastic pollution in harbors, rivers, and coastal areas.
Development of Garbage Collecting Robot for Marine Microplastics
Researchers developed a garbage-collecting robot designed to remove plastic debris from coastal areas before it degrades into microplastics, addressing the logistical challenge of cleaning extensive shorelines with minimal human labor and resources.
A Smart Garbage Classification based on Deep Learning
Researchers developed an AI-powered garbage classification system using deep learning to automatically sort waste categories. Accurate automated waste sorting could improve plastic recycling rates, reducing the amount of plastic that eventually breaks down into environmental microplastics.
Towards More Efficient EfficientDets and Real-Time Marine Debris Detection
Researchers improved the efficiency of a class of AI-based object detection systems called EfficientDets for real-time identification of marine debris underwater. Their optimized models achieved better accuracy while running faster, making them more practical for use on autonomous underwater vehicles. This technology could help enable automated detection and removal of ocean plastic waste, which breaks down into harmful microplastics over time.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
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.
Artificial intelligence-empowered collection and characterization of microplastics: A review
This review examines how artificial intelligence tools like robots and machine learning are being used to collect, identify, and characterize microplastic pollution more efficiently. Better detection technology matters for human health because accurately measuring microplastic contamination in water and soil is the first step toward understanding and reducing our exposure.
Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor
Scientists created a sensor that combines artificial intelligence with a specialized light-based probe to detect and identify different types of nano- and microplastics in water. The AI-powered system could distinguish between various plastic types with high accuracy, offering a faster and more practical way to monitor plastic contamination in drinking water and environmental samples.
AI-Enabled Plastic Pollution Monitoring System for Toronto Waterways
Researchers developed an AI-based monitoring system to detect plastic pollution in Toronto waterways using camera sensors. Automated AI monitoring enables continuous, large-scale tracking of plastic pollution, which is the precursor to the microplastics that accumulate in aquatic ecosystems.
Deep-Sea Debris Identification Using Deep Convolutional Neural Networks
Researchers developed a deep convolutional neural network classifier to identify and distinguish deep-sea debris from seafloor imagery, demonstrating that automated AI-based detection can support submersible clean-up operations targeting marine debris in deep-sea environments.
Aquatic Trash Detection and Classification: a Machine Learning and Deep Learning Perspective
This review examines machine learning and deep learning approaches for detecting and classifying aquatic trash in waterways, evaluating how computer vision algorithms trained on underwater and surface imagery can automate pollution monitoring for faster, more scalable ocean cleanup.
Automated Plastic Waste Detection Using Advanced Deep Learning Frameworks
Researchers developed a deep learning system using advanced neural network frameworks for automated detection and classification of plastic waste from images, achieving high accuracy in identifying multiple plastic types to support environmental monitoring and waste sorting.
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.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
Managing Marine Environmental Pollution using Artificial Intelligence
This review explores how artificial intelligence technologies are being developed to monitor and manage marine environmental pollution, including plastic contamination. The study suggests that AI-based approaches such as automated detection and predictive modeling offer promising opportunities for understanding ocean pollution and supporting sustainability goals.
Design and Method Research of Intelligent Detection System for Marine Microplastics Driven by Microfluidic Chip
Researchers designed an intelligent detection system for marine microplastics using a microfluidic chip combined with machine learning image analysis. Simulation testing validated the chip's ability to capture and sort microplastic particles from seawater samples, with AI classification achieving high accuracy across particle types.
Towards Accessible Aquatic Cleanup: A Low-Cost Solution for Floating Waste Extraction
Researchers designed and tested a low-cost autonomous floating waste extractor using a conveyor mechanism to capture lightweight surface pollutants including microplastics, demonstrating high efficiency in capturing debris and offering an affordable solution for resource-constrained settings.