For 3D microstructures fabricated by two-photon polymerization, a practical approach of
machine learning for detection and classification in their optical microscopic images is state
and demonstrated in this paper. It is based on Faster R-CNN, Multi-label classification (MLC)
and Residual learning framework Algorithms for reliable, automated detection and accurate
labeling of Two Photo Polymerization (TPP) microstructures. From finding and detecting the
microstructures from a different location in the microscope slide, matching different shapes of
the microstructures classify them among their categories is fully automated. The results are
compared with manual examination and SEM images of the microstructures for the accuracy
test. Some modifications of ordinary optical Microscope so as to make it automated and by
applying Deep learning and Image processing algorithms we can successfully detect, label and
classify 3D microstructures, designing the neural network model for each phase and by training
them using the datasets we have made, the dataset is a set of different images from different
angles and their annotation we can achieve high accuracy. The accurate microstructure
detection technique in the combination of image processing and computer vision help to
simulate the values of each pixel and classify the Microstructures.