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Papers
61,005 resultsShowing papers similar to Detection and Recognition of Ocean Garbage Using DIY ROV-Mounted DNN-Based Classification of Laser Images
ClearEnhancing 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.
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.
Design and Implementation of a Microplastic Detection and Classification System Supported by Deep Learning Algorithm
Researchers designed and implemented a low-budget deep learning system for autonomous microplastic detection and classification in water, using three dual-wavelength lasers at 405 nm, 655 nm, and 534-807 nm to classify microplastics by size and type in real time.
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.
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.
Identification of marine microplastics by laser-induced fluorescence spectroscopy: 1-Dimensional convolutional neural network and continuous convolutional model
Researchers investigated using laser-induced fluorescence spectroscopy combined with deep learning models to identify six types of marine microplastics. A continuous convolution neural network model achieved 99.5% classification accuracy, outperforming a standard 1D convolutional network at 97.5%. The approach offers a faster and less expensive alternative to traditional FTIR and Raman spectroscopy methods for microplastic identification.
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.
GoogLeNet-Based Deep Learning Framework for Underwater Microplastic Classification in Marine Environments
Researchers trained a GoogLeNet deep learning model on underwater images to classify microplastics into four categories, achieving strong classification performance for primary microplastics, secondary microplastics, non-microplastic debris, and marine biota in turbid coastal waters.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
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.
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.
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.
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.
Full stage networks with auxiliary focal loss and multi-attention module for submarine garbage object detection
Researchers developed a new AI-based detection system using modified YOLO neural networks to identify and locate garbage objects on the seafloor in complex underwater images. The system uses multi-scale feature extraction and a specialized loss function to accurately detect small and deformed debris items, supporting robotic clean-up efforts in marine environments.
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.
Plastic Waste on Water Surfaces Detection Using Convolutional Neural Networks
Researchers evaluated state-of-the-art convolutional neural network architectures for automatically detecting plastic waste on water surfaces, training models on a dataset representing four categories of plastic litter including plastic bags. The study benchmarked multiple CNN object detection models following extensive dataset preprocessing to determine the most effective approach for automated plastic pollution identification.
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.
Designing a Low-Cost Microcontroller-Based Rover for Microplastic Detection Using Deep-Learning Image Detection and Near-Infrared Spectroscopy
Researchers designed a low-cost microcontroller-based rover for detecting nurdle microplastics in shoreline environments, integrating a compressed deep-learning object detection model trained on 150 images of polyethylene pellets with an AS7263 near-infrared sensor for spectral confirmation of polyethylene. The Raspberry Pi 3-based system demonstrated efficient microplastic identification across varying lighting conditions and burial depths in sand.
Underwater Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning
Researchers reviewed machine learning techniques for underwater image detection to support water pollution cleanup, focusing on convolutional neural networks and region-based CNN methods for identifying surface mucilage and debris. The study evaluated supervised classification algorithms as the most effective approach for automated aquatic waste detection systems.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
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.
Rapid identification of marine microplastics by laser-induced fluorescence technique based on PCA combined with SVM and KNN algorithm
Researchers developed a laser-based fluorescence method combined with machine learning algorithms to rapidly identify different types of marine microplastics. The system achieved classification accuracy above 97 percent for four common plastic types at various concentrations. The technique offers a fast, non-destructive alternative to traditional laboratory methods for monitoring microplastic pollution in ocean environments.
Amphitrite v1.0: An underwater database for marine debris and biodiversity
Researchers created Amphitrite v1.0, an underwater database of 16 labeled categories of marine debris and biodiversity images suitable for training computer vision models for automated marine litter detection, validated against the latest YOLO object recognition architectures.