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Papers
20 resultsShowing papers similar to Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms
ClearDeep-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.
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.
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.
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.
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.
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.
Underwater and airborne monitoring of marine ecosystems and debris
Researchers demonstrated that the deep-learning object detection algorithm YOLO v3 can detect underwater sea life and floating marine debris with mean average precision of 69.6% and 77.2% respectively, using autonomous underwater and aerial robots. The study proposes this approach as a foundation for scalable autonomous monitoring systems capable of tracking marine ecosystems and plastic debris across oceanographic scales.
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.
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.
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.
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.
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 Characterisation of Benthic Currents from Seabed Bathymetry: An Object-Based Image Analysis of Cold-Water Coral Mounds
Researchers used object-based image analysis of multibeam sonar data to characterize seafloor bedforms and infer benthic current directions and speeds around cold-water coral mounds. The approach automatically classifies seafloor features and links them to oceanographic conditions. Better mapping of deep-sea currents helps explain how marine organisms and particles — including potential plastic debris — are transported along the seafloor.
Image recognition of microplastic particles in marine sediments – planned activities
This abstract outlines a planned research effort to develop image recognition algorithms for automatically identifying microplastic particles in marine sediment samples. Automated identification could greatly speed up the labor-intensive task of quantifying microplastics in sediment, enabling broader and more consistent environmental monitoring.
Efficient Classification of Marine Debris using SVM with Noise Removal and Feature Extraction Techniques with Improved Performances
This study evaluated different image processing filters for reducing noise in underwater photos of marine debris, then applied support vector machine (SVM) classification to automatically identify debris types. Automated marine debris detection technology is important for scaling up plastic pollution monitoring in ocean environments.
Data Study Group Final Report: Centre for Environment, Fisheries and Aquaculture Science
Machine learning was applied to the challenge of automatically classifying plankton species from underwater images collected by fisheries monitoring systems. The AI classifier could identify dozens of plankton categories with high accuracy, reducing the need for time-consuming manual identification. Automated plankton monitoring improves understanding of marine food web health and ecosystem responses to environmental change.
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.
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.
Digital Image Identification of Plankton Using Regionprops and Bagging Decision Tree Algorithm
Researchers developed a digital image classification system using machine learning to identify and count plankton from microscopy images. The method reduced the time and subjectivity of manual identification while maintaining accuracy. Automated plankton identification could also be adapted to distinguish microplastics from biological particles in environmental water samples.
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.