This work uses a deep learning approach using convolutional neural networks to locate and classify nanostructures in a heterogenous composition material from TEM imaging. We developed a methodology that allowed us to create 533 ground truth of TEM images with three different classes: 1) silicon oxide nanoparticles, 2) yttrium silicate particles and 3) silicon oxide coating. We performed the classification, location, and segmentation of chemical compounds reaching scores above 80% of accuracy using Mask R-CNN architecture with Anaconda Python 3.7 and the Tensorflow framework under Windows 10.
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