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Papers
61,005 resultsShowing papers similar to A Machine Arm to Assist in Trash Sorting using machine Learning and Object Detection
ClearArtificial Intelligence-Based Robotic Technique for Reusable Waste Materials
This paper describes an AI-based robotic arm system that uses a customized deep learning model to classify and sort waste materials including plastics and cartons by material type for automated recycling. The integrated system combines gripping, motion control, and AI-driven material classification into a full-automation architecture for waste recovery.
Enhancing Waste Management with a Deep Learning-based Automatic Garbage Classifier
This paper is not about microplastics; it presents a deep learning convolutional neural network system for automatically classifying garbage by material type to improve waste sorting efficiency and reduce the labor burden of manual waste management.
Design and Fabrication of Material Separation Machine for Sustainable Development
This paper is not relevant to microplastics research — it describes the design and fabrication of a robotic material separation machine intended to sort recyclable waste more efficiently using AI-inspired engineering principles.
Application of AI-Enabled Computer Vision Technology for Segregation of Industrial Plastic Wastes
Researchers developed an AI-powered computer vision system to segregate mixed industrial plastic wastes by polymer type, addressing a key barrier to effective plastic recycling. The system achieved high classification accuracy across common plastic categories, demonstrating that machine vision can improve sorting efficiency and recycled plastic quality.
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.
An Automatic Garbage Classification System Based on Deep Learning
Researchers developed an automated garbage classification system using a deep learning algorithm based on ResNet-34, achieving 99% classification accuracy with a processing time of under one second per item. Automated waste sorting technology like this could improve the efficiency of plastic waste recovery and reduce mismanaged plastic that eventually becomes environmental pollution.
Autonomous detection and sorting of litter using deep learning and soft robotic grippers
Researchers developed LitterBot, an autonomous robotic system that uses deep learning-based object detection and segmentation to identify, localize, and classify common roadside litter, and pairs this with soft robotic grippers to automate the collection process, addressing the labor-intensive and hazardous nature of roadside litter picking.
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.
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.
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.
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.
Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste
This paper describes using data augmentation techniques to improve machine learning models for automated sorting of litter in outdoor environments. Better waste sorting technology could improve plastic recycling rates and reduce the amount of plastic that ends up fragmenting into microplastics.
Sensor-based and Robot Sorting Processes and their Role in Achieving European Recycling Goals - A Review
This review covers sensor-based and robotic sorting technologies for waste management, assessing how they can help achieve European recycling targets. Improved sorting is critical for increasing plastic recycling rates and reducing the amount of plastic that enters the environment as pollution.
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.
A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification
Researchers developed a deep learning computer model that can sort waste into six categories, including plastic, with 95% accuracy. While this is a waste management technology rather than a health study, better automated waste sorting could help keep more plastics out of the environment where they break down into microplastics. Improved recycling through AI-powered sorting is one practical step toward reducing the microplastic pollution that eventually reaches people.
A novel autonomous microplastics surveying robot for beach environments
Researchers developed a novel autonomous robotic platform for detecting and chemically analyzing microplastics on beach surfaces, using a camera mounted on a robotic arm end effector to scan areas and identify particles smaller than 5 mm. The mobile manipulator system automatically locates and chemically characterizes microplastics in situ, addressing the challenge of large-scale environmental monitoring in coastal environments.
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.
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.
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.
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.
Development of Garbage Collecting Robot for Marine Microplastics
Researchers designed and developed an autonomous cleaning robot for collecting marine microplastics scattered on beaches, using a conveyor belt and tray system to mechanically gather and retain small plastic particles. The study addresses the practical difficulty of manually collecting dispersed microplastics and demonstrates the robot's configuration and operational concept for beach remediation.
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.
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.
Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images
Researchers applied machine learning to aerial multispectral images for automated detection of plastic litter in natural areas, demonstrating that combining spectral data with classification algorithms can effectively identify and monitor plastic waste pollution.