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Papers
61,005 resultsShowing papers similar to From Local to Global: Efficient Dual Attention Mechanism for Single Image Super-Resolution
ClearVision Transformer Model in Environmental Surveillance: Detection of Microplastics for Global Sustainability
Researchers developed a deep learning model using Vision Transformers within a Fast R-CNN framework to automatically detect and quantify microplastics from images. The hybrid model showed strong performance in detecting particles 3 millimeters or larger but had difficulty identifying very small particles. The approach offers a faster and potentially more efficient alternative to traditional laboratory methods for microplastic identification.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images
Researchers applied deep learning-based super-resolution networks to enhance the spatial resolution of AMSR2 passive microwave satellite images of Arctic sea ice, addressing the coarse native resolution that limits detailed analysis of sea ice extent and dynamics. The approach improved image quality and enabled more precise monitoring of Arctic sea ice changes with implications for understanding climate impacts on polar ecosystems.
Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism
Not relevant to microplastics — this paper develops a deep-learning image fusion method to improve underwater target detection using polarization cameras, with no connection to plastic pollution.
Hyperspectral detection of soil microplastics via multimodal feature fusion and a dual-path attention residual convolutional network
A hyperspectral imaging approach combined with multimodal deep learning was developed to detect microplastics in soil, achieving high accuracy in identifying plastic particles against complex soil backgrounds. The method offers a faster, less destructive alternative to traditional chemical extraction and spectroscopy for soil monitoring.
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.
A Deep Learning Approach for Microplastic Segmentation in Microscopic Images
Researchers developed a deep learning model for automated segmentation and classification of microplastics in microscopic images, identifying five distinct categories including fibers, fragments, spheres, foam, and film. The model achieved high accuracy while maintaining low computational requirements, making it suitable for high-throughput deployment in environmental monitoring. The study offers a tool that could help overcome the measurement bottleneck in microplastic characterization for toxicological and risk assessment studies.
Microplastic Spectral Classification Using Deep Learning with Denoising and Dimensionality Reduction
Researchers developed a deep learning approach for microplastic spectral classification that incorporates denoising and dimensionality reduction steps, improving the accuracy of identifying and classifying microplastic polymer types from spectral data in marine ecosystems.
On the use of deep learning for phase recovery
Researchers reviewed how deep learning — a type of artificial intelligence — can recover phase information from light, which is typically lost when cameras capture images, enabling sharper microscopy and better materials analysis. These advances improve the tools scientists use to study tiny particles, including microplastics, at very fine scales.
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.
Marine Debris Detection in Satellite Surveillance Using Attention Mechanisms
Researchers developed an approach combining satellite imagery with attention-based deep learning models to detect marine debris from space. The study found that a model using combined spatial and channel attention (CBAM) achieved the best performance, with a 77% detection score, outperforming other approaches tested. These findings suggest that AI-enhanced satellite surveillance could become a practical tool for monitoring ocean plastic pollution at scale.
Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques
Researchers developed a new method for detecting and characterizing microplastics using photoacoustic imaging combined with deep learning algorithms. The approach enables high-resolution visualization of microplastic morphology and distribution in environmental samples. The study suggests that this integrated imaging and AI technique could significantly advance environmental monitoring capabilities for tracking microplastic contamination.
Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data
Researchers developed an innovative method combining photoacoustic imaging with deep learning to rapidly detect and characterize microplastics. The photoacoustic technology captured high-resolution images of diverse microplastic samples, while the neural network automated the classification process. The study demonstrates that this combined approach could enable faster, more accurate microplastic monitoring compared to conventional methods.
Deep Classification of Microplastics Through Image Fusion Techniques
Deep neural networks were applied to classify microplastic fibers captured via digital holography microscopy, using image fusion techniques on the Holography Micro-Plastic Dataset benchmark. The study demonstrated promising accuracy for distinguishing microplastics from other debris, advancing automated microplastic identification in water quality monitoring.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
Proceeding the categorization of microplastics through deep learning-based image segmentation
Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
Efficient Microplastic Detection in Water Using ResNet50 and Fluorescence Imaging
Researchers applied a ResNet50 deep learning model to fluorescence microscopy images of water samples, achieving high-accuracy classification of microplastics, demonstrating that deep learning can efficiently automate microplastic identification from microscopy data.
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.
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.
Slim Deep Learning Approach for Microplastics Image Classification in the Marine Environment
Researchers developed a lightweight convolutional neural network called the Slim-DL-Model for classifying microplastics in marine environment images, designed to overcome the computational demands of existing architectures like VGG16 and ResNet for real-time field applications. The model achieves competitive classification accuracy while significantly reducing computational requirements, enabling deployable microplastic monitoring systems.
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.
Hybrid deep learning framework for environmental microplastic classification: Integrating CNN-based spectral feature extraction and transformer models
Researchers developed a hybrid deep learning framework combining convolutional and attention-based architectures to classify environmental microplastics from FTIR spectra, achieving improved accuracy on weathered and contaminated samples that challenge conventional spectral library approaches.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.