Development of High Accuracy Segmentation Model for Microstructure of Steel by Deep Learning

Abstract

We studied on automation of segmentation using deep learning, which has been remarkably developed in recent years.

For the microstructural image of ferrite-martensite dual phase steel, we tried to segment the ferrite phase, martensite phase, and ferrite grain boundary in different colors individually. We created two models, SegNet and U-Net that can perform segmentation with high accuracy and compared the accuracy with an existing method.

As a result, we demonstrated that models using deep leaning is more accurate than the existing method. In particular, U-Net model shows highly accuracy of segmentation for material microstructures.