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Papers
61,005 resultsShowing papers similar to Holographic imaging and machine learning for microplastic size and shape analysis in water
ClearHigh-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.
Microplastic Identification via Holographic Imaging and Machine Learning
Researchers combined holographic imaging with machine learning algorithms to automatically identify and classify microplastics in water samples, achieving accurate particle detection without manual microscopy. This automated approach could significantly speed up microplastic monitoring in environmental samples.
Microplastic pollution monitoring with holographic classification and deep learning
This study used digital holographic microscopy combined with deep learning to classify microplastic particles in water samples, achieving high classification accuracy and demonstrating the potential for automated, high-throughput microplastic monitoring.
Digital holographic imaging and classification of microplastics using deep transfer learning
Researchers developed a digital holographic imaging system combined with deep learning to automatically classify and analyze microplastic particles in water samples. Automated imaging and AI-based identification could significantly speed up and standardize microplastic monitoring, reducing the labor-intensive manual counting currently required.
Automatic Detection of Microplastics by Deep Learning Enabled Digital Holography
Researchers developed a digital holography system combined with deep learning to automatically detect and identify microplastics in water without manual image analysis. The system processes raw holographic images directly, offering a faster and more scalable approach to microplastic monitoring in environmental samples.
Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images
Researchers developed a machine learning system that uses holographic 3D images to automatically classify microplastics in water samples, distinguishing them from other microscopic particles with high precision. Current microplastic monitoring is slow and labor-intensive, so automated detection tools are essential for large-scale environmental surveillance. This approach could significantly speed up the monitoring of microplastic pollution in aquatic environments.
Computational Polarimetric Holography for Efficient Microplastic Classification via a Lightweight Wavelet-Enhanced Vision Transformer
Researchers built a portable device that uses light patterns (holographic polarimetry) to identify different types of microplastics in water, then trained a lightweight AI model to classify them with 97.97% accuracy while cutting computing power needs by over half. This breakthrough could enable real-time microplastic monitoring directly in the field, rather than in a laboratory.
Holographic Classifier: Deep Learning in Digital Holography for Automatic Micro-objects Classification
Researchers developed a deep learning system using digital holography to automatically classify micro-objects such as microplastics and pollutant particles without manual image processing. The system achieved fast, accurate identification, offering a promising automated tool for environmental pollution monitoring.
Polarization Holographic Imaging for High-throughput Microplastic Analysis
Researchers developed a polarization holography system integrated with deep learning for high-throughput microplastic detection and analysis in aqueous environments. The system enables dynamic, real-time multimodal monitoring of microplastics by leveraging polarization contrast to distinguish particles in liquid samples.
Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning
Researchers used digital holographic microscopy combined with deep learning to detect and characterize microplastic particles in heterogeneous samples containing algae, microorganisms, and other natural particles. This automated approach could improve the speed and accuracy of environmental microplastic monitoring.
Identification of Microplastics Based on the Fractal Properties of Their Holographic Fingerprint
Researchers developed an AI-enabled holographic imaging approach to identify microplastics in water using the fractal properties of their holographic fingerprints, offering a fast, label-free identification method.
Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection
Researchers developed an artificial intelligence-powered holographic microscopy system that can detect and classify nanoplastics in water in real time, without any sample preparation. The technology identified particles as small as 135 nanometers and tracked their movement in three dimensions. This represents a significant advancement in environmental monitoring, as previous methods required extensive lab processing to detect plastic particles this small.
Digital holographic approaches to the detection and characterization of microplastics in water environments
This review examines advances in using digital holography as a high-throughput tool for detecting and characterizing microplastics in water. Researchers discuss both the hardware and software developments, including the growing role of artificial intelligence for classification tasks. The study highlights the emergence of field-portable holographic flow cytometers as a promising technology for real-time water monitoring of microplastic contamination.
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.
Intelligent Digital Holographic systems to counteract microplastic pollution in marine waters
Researchers developed a digital holography system capable of detecting and classifying microplastic particles in seawater in a label-free, high-throughput manner. The system can identify plastic particles that are otherwise invisible to the naked eye and can be adapted for use with microfluidic devices. This technology offers a faster and more compact alternative to traditional microscopy methods for marine microplastic monitoring.
Microplastic pollution assessment with digital holography and zero-shot learning
Researchers developed a digital holography system combined with zero-shot machine learning to identify and characterize microplastics in environmental samples without requiring labeled training data, offering a promising automated tool for large-scale microplastic monitoring.
Smart polarization and spectroscopic holography for real-time microplastics identification
Researchers developed a new optical imaging system called SPLASH that simultaneously captures polarization, holographic, and texture data from tiny particles — without needing a traditional spectrometer — and used machine learning to identify different types of microplastics with high accuracy. This approach could enable faster, more practical real-time monitoring of microplastic pollution in water.
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.
Enhanced classification of microplastic polymers (polyethylene, polystyrene, low‐density polyethylene, polyhydroxyalkanoate) in waterbodies
Researchers developed a new deep learning model to automatically detect and classify different types of microplastic polymers in water from holographic images. The system combines advanced image segmentation with a vision transformer to identify polyethylene, polystyrene, low-density polyethylene, and polyhydroxyalkanoate particles. The approach aims to improve the speed and accuracy of microplastic monitoring in aquatic environments compared to traditional manual methods.
Material analysis with polarization holography and machine learning
Researchers developed a polarization holographic imaging system combined with machine learning to identify different materials, demonstrating the approach on microplastic identification. This novel optical method could become a fast, non-destructive tool for classifying microplastics in environmental samples.
Compact holographic microscope for imaging flowing microplastics
Researchers developed a compact holographic microscope capable of imaging flowing microplastics in aquatic environments, providing a fast, quantitative method for real-time characterization of plastic particle size and shape distributions.
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.
Holographic and polarization features analysis for microplastics characterization and water monitoring
Researchers explored digital holography and polarization imaging as a combined technique for characterizing and classifying microplastics in water, computing features including angle of polarization (AoP) and degree of linear polarization (DoLP) to distinguish microplastics from biological and natural particles. The method demonstrated potential for real-time, non-contact, in situ microplastic detection and water quality monitoring.
On the use of machine learning for microplastic identification from holographic phase-contrast signatures
This study applied machine learning to identify microplastic types from holographic phase-contrast imaging signatures, achieving rapid automated classification. Automated identification tools are important for scaling up microplastic monitoring in marine waters where manual identification is too slow and labor-intensive.