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Keywords = AI-driven real-time microscopic image processing

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28 pages, 7966 KB  
Article
Real-Time Edge Computing vs. GPU-Accelerated Pipelines for Low-Cost Microscopy Applications
by Gloria Bueno, Lucia Sanchez-Vargas, Alberto Diaz-Maroto, Jesus Ruiz-Santaquiteria, Maria Blanco, Jesus Salido and Gabriel Cristobal
Electronics 2025, 14(5), 930; https://doi.org/10.3390/electronics14050930 - 26 Feb 2025
Cited by 6 | Viewed by 3104
Abstract
Environmental microscopy is crucial for analyzing microorganisms, but traditional optical microscopes are often expensive, bulky, and impractical for field use. AI-driven image recognition, powered by deep learning models like YOLO, enhances microscopy analysis but typically requires high computational resources. To address these challenges, [...] Read more.
Environmental microscopy is crucial for analyzing microorganisms, but traditional optical microscopes are often expensive, bulky, and impractical for field use. AI-driven image recognition, powered by deep learning models like YOLO, enhances microscopy analysis but typically requires high computational resources. To address these challenges, we present two cost-effective pipelines integrating AI with low-cost microscopes and edge computing. Both approaches use the OpenFlexure Microscope and Raspberry Pi devices. The first performs real-time inference with a Raspberry Pi 5 and Hailo-8L accelerator, while the second captures images with a Raspberry Pi 4, transferring them to a GPU-equipped desktop for processing. Using YOLOv8, we evaluate their ability to detect phytoplankton species, including cyanobacteria and diatoms. Results show that edge computing enables accurate, efficient, and low-power microscopy analysis, demonstrating its potential for real-time environmental monitoring in resource-limited settings. Full article
(This article belongs to the Special Issue Real-Time Computer Vision)
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