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Papers
20 resultsShowing papers similar to Detection of Trash in Sea Using Deep Learning
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 Recognition of Ocean Garbage Using DIY ROV-Mounted DNN-Based Classification of Laser Images
Researchers designed a low-cost DIY underwater robot equipped with a laser imaging system and deep learning classifier to detect and categorize underwater garbage from microplastics to large debris. A custom-trained convolutional neural network achieved 91% classification accuracy, outperforming transfer learning approaches.
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.
An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment
Researchers developed an improved object detection algorithm based on Mask R-CNN with dilated convolution in the feature pyramid network to enable more accurate automated identification and segmentation of marine garbage by underwater detectors. The embeddable algorithm is designed to function effectively in complex, low-resolution underwater environments.
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.
Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks
Researchers developed convolutional neural network models for efficiently identifying and segmenting microplastics in urban water samples from southern China. The study found that deep learning approaches can significantly reduce the time and labor required for microplastic identification compared to manual methods, offering a scalable tool for monitoring microplastic pollution in urban waterways.
Real-Time Instance Segmentation for Detection of Underwater Litter as a Plastic Source
Researchers developed a real-time instance segmentation system using neural networks to detect underwater plastic litter on the seafloor, targeting the approximately 70% of marine litter that sinks and serves as a major source of ocean microplastics.
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.
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.