Automatic Identification of the Multiphase Microstructures of Steels based on ASPP‐FCN

Abstract

The properties of steels are often closely related to the category and distribution of their microstructures. However, the classification and quantitative analysis of multiphase steel are mostly performed manually, which is time‐consuming and laborious. Moreover, due to the variant experience of experts, the classification results of one image will be different. In this article, an automatic classification model is proposed to identify the multiphase microstructures of steels by means of semantic segmentation, which realizes the automatic statistics of the relative content of each crystalline phase. Semantic segmentation can assign each pixel in an image to one of a set of predefined categories, which can accomplish two tasks concurrently: phase identification and segmentation. We propose an improved fully convolutional network ASPP‐FCN, and carry out comparative experiments with different networks, including FCN, DeepLab v3+, Unet, Enet and PSPnet. The final pixel accuracy of ASPP‐FCN in each category is more than 95%; and the mean intersection over union is over 80%, the results demonstrate that ASPP‐FCN has better segmentation effect in the automatic recognition of the multiphase microstructures of steels.