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Papers
20 resultsShowing papers similar to Development of Drifting Debris Detection System using Deep Learning on Coastal Cleanup
ClearAn 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.
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.
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.
Detection of Trash in Sea Using Deep Learning
Researchers developed a deep learning convolutional neural network (CNN) model to detect and classify trash in marine and aquatic environments from underwater images, aiming to overcome the limitations of manual debris detection for objects that may be submerged or partially obscured.
Large-scale detection of marine debris in coastal areas with Sentinel-2
Researchers built a deep learning model to detect floating marine debris in coastal areas using satellite imagery from the Sentinel-2 program. The system achieved strong detection accuracy across multiple test sites and can monitor large stretches of coastline regularly. The tool could help environmental agencies track and respond to marine plastic pollution at a scale that manual surveys cannot match.
Advancing Plastic Pollution Monitoring Through Enhanced Protocols and Deep Learning: applicability and effectiveness in real-world scenarios (Le Stang, France)
Researchers developed and tested a deep learning image analysis tool to enhance monitoring of beach plastic pollution, specifically targeting meso- and large microplastics at the wrack line in Brittany, France. The AI model achieved high detection accuracy under real-world conditions and integrated with established French national monitoring protocols, demonstrating feasibility for scalable automated beach litter surveillance.
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.
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.
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.
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.
Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
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.
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.
Enhancing marine debris identification with convolutional neural networks
A deep learning model was developed to identify and classify marine debris components captured by underwater remotely operated vehicle imagery, addressing the challenge of widely distributed ocean waste including microplastics. The convolutional neural network demonstrated improved accuracy for debris detection and classification compared to conventional image analysis methods.
Projector deep feature extraction-based garbage image classification model using underwater images
Researchers developed a deep learning model using projector-based feature extraction to classify underwater garbage images, achieving high accuracy in identifying marine plastic debris and other waste types for automated ocean pollution monitoring.
Litter segmentation with LOTS dataset
Not a microplastics paper — this computer science paper presents a machine learning benchmark for detecting and segmenting beach litter (including plastic debris) in sand using deep learning image segmentation models, contributing tools that could help automate coastal pollution monitoring.
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.
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.
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.