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Papers
61,005 resultsShowing papers similar to A Proposed Technology Solution for Preventing Marine Littering Based on Uavs and Iot Cloud-based Data Analytics
ClearUnmanned Vehicles System Utilizing Waste Tracking Data to Tackle Plastic Marine Littering on Tourist Islands
This paper proposes using unmanned vehicles guided by waste tracking data to collect plastic marine litter around tourist islands. Autonomous cleanup technology could help remove plastic debris before it breaks down into microplastics and enters the food chain.
Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
Researchers developed an object-oriented machine learning classification strategy using unmanned aerial system imagery to automatically identify and quantify marine macro litter on sandy beaches, comparing three automated methods against manual counts. The UAS-based approach demonstrated capacity for scalable, cost-effective beach litter monitoring to support coastal pollution surveillance programs.
Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination
Researchers tested drone-based aerial surveys with high-resolution cameras as a cost-effective method for monitoring floating litter contamination in coastal waters, comparing manual counting, automated detection, and modeling approaches to optimize survey design.
UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)
UAV imaging was used to detect and map anthropogenic marine debris on beaches in the Po River Delta, Italy, testing different image processing strategies and demonstrating that centimeter-scale spatial resolution UAV surveys can efficiently locate macroplastics before they degrade into harder-to-remove microplastics.
Unmanned Aerial Vehicles for Debris Survey in Coastal Areas: Long-Term Monitoring Programme to Study Spatial and Temporal Accumulation of the Dynamics of Beached Marine Litter
Researchers used UAVs to acquire georeferenced RGB images over a ten-month monitoring programme at a protected marine area near Pisa, Italy, to characterize the spatial and temporal distribution of beached marine debris. Post-processing via visual interpretation allowed localization and identification of anthropogenic debris accumulation patterns, demonstrating UAV-based surveying as an effective low-impact approach for long-term coastal litter monitoring.
Use of UAVs and Deep Learning for Beach Litter Monitoring
Researchers developed an autonomous beach litter monitoring pipeline using UAV drone surveys combined with a YOLOv5 deep learning object detection algorithm trained on footage from Malta, Gozo, and the Red Sea coast. The system achieved a mean average precision (mAP50-95) of 0.252 across all litter classes and incorporated geolocation and digital elevation model data to support future autonomous retrieval robots.
Object Detection of Macroplastic Waste Using Unmanned Aerial Vehicles in Urban Canal
Researchers developed and tested an unmanned aerial vehicle-based system for detecting macroplastic waste along riverbanks and beaches using object detection algorithms. The system achieved reliable detection performance and offers a scalable tool for large-area plastic litter surveys.
Mini Uav-based Litter Detection on River Banks
Researchers developed a drone-based litter detection system combining high-resolution mapping, deep learning object detection, and vision-based localization that locates riverbank litter with decimeter-level accuracy, enabling automated monitoring of plastic pollution in urban waterway areas.
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.
Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
Researchers developed a convolutional neural network-based algorithm to automatically detect and quantify floating marine macro-litter in aerial images, training it on 3,723 images and integrating it into a web application for practical monitoring use.
Detection and assessment of marine litter in an uninhabited island, Arabian Gulf: A case study with conventional and machine learning approaches
Researchers surveyed marine litter on a remote Arabian Gulf island after a large cleanup, then trained a YOLO-v5 deep learning model on 10,400 beach images to automatically detect debris, achieving 90% detection accuracy and demonstrating that windward shores accumulate significantly more litter from neighboring countries.
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.
Assessment of marine litter through remote sensing: recent approaches and future goals
This review classified remote sensing approaches for detecting marine litter — including satellite, aircraft, and drone platforms with optical, infrared, and radar sensors — finding that few studies had reached operational status and that detecting small or submerged litter remains a major technical challenge.
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.
Use of Mobile Autonomous Systems for Pollution Control of Inland Water Bodies
Researchers examined the use of mobile autonomous aerial and floating systems for monitoring and controlling pollution in inland water bodies, including detection of illegally dumped construction and household waste that contributes to microplastic and groundwater contamination. The study analyzes existing practices and proposes improvements for using drones and autonomous surface vehicles to enable early detection of unregulated dumping with minimal resources.
Citizen Science Protocol for beach plastic monitoring using aerial drones
Researchers developed a citizen science protocol using aerial drones to monitor plastic pollution on beaches. The study outlines systematic methods for community-based beach surveys to track the accumulation of plastic debris, from large items to microplastics, supporting environmental monitoring efforts along coastal areas.
A Mobile Application to Assist in Reporting and Cleaning Spots of Ocean Litters using Machine Learning
Researchers developed a mobile application that uses machine learning to help users report and locate ocean litter, aiming to improve community-driven cleanup efforts and generate spatial data on marine plastic pollution.
Beach Cleaning Robots a Comprehensive Survey of Technologies Challenges, and Future Directions
This paper is not relevant to microplastics; it is a survey of robotic technologies and methodologies for automated beach cleaning and litter removal.
Citizen Science Protocol for beach plastic monitoring using aerial drones
Researchers developed a citizen science protocol using aerial drones to monitor plastic pollution on beaches. The study outlines methods for engaging community volunteers in systematic beach surveys, aiming to improve the scale and frequency of plastic pollution data collection for environmental monitoring and policy development.
Towards the Spectral Mapping of Plastic Debris on Beaches
This paper reviews the use of remote sensing (satellite and aerial imaging) to detect and map plastic debris on beaches. Advances in spectral imaging could allow large-scale automated monitoring of coastal plastic pollution, which is currently labor-intensive and limited in coverage.
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.
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.
Developing Beach Litter Monitoring System Based on Reflectance Characteristics and its Abundance
Researchers developed a beach litter monitoring system using optical reflectance characteristics of plastic debris, training a remote sensing model to detect and classify litter items on sandy beach surfaces. The system demonstrated accurate detection of common plastic litter types and offers a scalable, automated alternative to manual beach surveys.
AI-Driven UAV Systems for Real-Time Detection and Monitoring of Airborne Microplastics in Dhaka, Bangladesh
Researchers proposed an AI-integrated UAV system using machine learning, hyperspectral imaging, and remote sensing for real-time detection of airborne microplastics in Dhaka, Bangladesh, with preliminary results suggesting up to 95% detection accuracy as a scalable alternative to laboratory-based methods.