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Sizing Microplastic Particles Using Acoustic Imaging and Deep Neural Network
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Researchers developed an acoustic imaging-based deep neural network system to size microplastic particles in real time, comparing Total Focusing Method and Circular Wave Imaging strategies and achieving accurate particle segmentation from acoustic signals.
This study addresses microplastic pollution in oceans by developing a real-time sensor system to size microplastic particles from aquatic environments. Using a deep neural network model, the study aims to segment microplastic particles from acoustic images. The methodology involves acoustic imaging with ultrasonic multi-element probes, utilizing imaging strategies like Total Focusing Method (slow but accurate) and Circular Wave Imaging (fast but includes artifacts). By generating a dataset of acoustic simulations, we trained multiple deep neural network models using various image reconstruction strategies to evaluate the feasibility of sizing and counting particles through rapid measurements, even with degraded reconstructed images. The findings suggest that deep-learning-based acoustic imaging can enhance the monitoring of oceanic microplastics by potentially increasing frame rates and simplifying probe complexity. Additionally, the model succeeds on experimental data, not included in the training set, which shows good generalization of the model.
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Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data
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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.
High-frequency ultrasound combined with deep learning enables identification and size estimation of microplastics
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Researchers developed a high-frequency ultrasound method combined with deep learning — using a 1D convolutional neural network on spectral and temporal echo features — to identify microplastic material type and estimate particle size, offering a faster and non-destructive alternative to FTIR and Raman spectroscopy.
Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques
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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.
High-frequency ultrasound combined with deep learning enables identification and size estimation of microplastics
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Scientists developed a new method using sound waves and artificial intelligence to quickly detect tiny plastic particles (microplastics) in the environment with over 97% accuracy. This technology could help us better monitor microplastic pollution in water and food sources, which is important since these particles can end up in our bodies through what we eat and drink. The new method is much faster than current testing approaches, making it easier to track plastic pollution on a large scale.
Automatic Detection of Microplastics in the Aqueous Environment
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Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.