Microsoft Word - SREP-19-11906_manuscript_revised.doc We demonstrate the feasibility of automated identification of common crystallographic defects in steels using deep learning semantic segmentation, based on high-quality microscopy data. In particular, the DefectSegNet - a new hybrid CNN architecture with skip connections within and across the encoder and decoder was developed, and has proved to be effective at perceptual defect identification with high pixel-wise accuracy.