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A Comparative Study of State-of-the-Art Deep Learning Models for Semantic Segmentation of Pores in Scanning Electron Microscope Images of Activated Carbon
Summary
Researchers developed deep learning models to automatically identify and measure pores on activated carbon surfaces from electron microscope images. The study introduced a new dataset and tested several state-of-the-art models for this task, showing promising results compared to slow and costly manual analysis. The findings suggest that AI-based approaches could make quality assessment of activated carbon more efficient for applications in water purification and air filtration.
Accurate measurement of the microspores, mesopores, and macropores on the surface of the activated carbon is essential due to its direct influence on the material’s adsorption capacity, surface area, and overall performance in various applications like water purification, air filtration, and gas separation. Traditionally, Scanning Electron Microscopy (SEM) images of activated carbons are collected and manually annotated by a human expert to differentiate and measure different pores in the surface. However, manual analysis of such surfaces is costly, time-consuming, and resource-intensive as requires supervision from experts. In this paper, we propose an automatic Deep-learning-based solution to address this challenge of activated carbon surface segmentation. We introduce a novel SEM Image segmentation dataset for activated carbon. We then evaluate the state-of-the-art deep learning models on the novel semantic segmentation task that shows promising results. Finally, we outline the key research challenges and discuss potential research directions to address these challenges.
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