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Papers
20 resultsShowing papers similar to Automatic Detection of Microplastics by Deep Learning Enabled Digital Holography
ClearMicroplastic 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.
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.
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.
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.
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.
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.
High-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.
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.
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.
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.
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.
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.
Holographic imaging and machine learning for microplastic size and shape analysis in water
Researchers used a portable holographic camera paired with deep-learning AI to rapidly measure the size and shape of microplastics floating in water, finding the lightweight MobileNetV2 model outperformed the larger ResNet101 in classification accuracy. The method offers a cost-effective, field-deployable tool for monitoring microplastics in drinking water at scale.
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.
Complete holography‐based system for the identification of microparticles in water samples
Researchers developed a comprehensive holography-based system for identifying and classifying microparticles — including microplastics — in water samples using microscopic holographic projections, designed for researchers without specialist holography expertise. The system is deployable as part of an autonomous sailboat robot for large-scale environmental monitoring of diverse microplastic types in water bodies.
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.
Real-time microplastic detection using polarization digital holographic microscope
Researchers developed a real-time microplastic detection system using a polarization digital holographic microscope, enabling identification and characterization of MP particles in water based on their optical properties without the need for chemical staining or extensive sample preparation.
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.
Detection of microplastic release into water from plastic containers based on lensless digital holography
Researchers used lensless digital holography to detect microplastics released from plastic food delivery containers into water, demonstrating that the technique can differentiate microplastic particles from other impurities and quantify their release under realistic conditions.
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.