Recent achievements of the team - Automatic segmentation and recognition of the microstructure of high-strength low-alloy steel
Our team published the paper “Automatic Segmentation and Recognition of the Microstructure of High-Strength Low-Alloy Steel” in Materials (CAS Zone 3, JCR Q2). This paper focuses on the automatic segmentation and recognition of the microstructure of high-strength low-alloy steel.
Metallographic microstructure analysis is important for revealing the rules of microstructural evolution in steel during heat treatment and mechanical processing. However, optical microscopy images commonly suffer from blurred grain boundaries, uneven grayscale distribution inside grains, and irregular grain morphology, which pose challenges to accurate microstructure analysis. To address these issues, this paper proposes an automated metallographic image processing method based on superpixels, named DPSS (Dual-Phase Steel Segmentation), with a focus on high-quality microstructure segmentation and subsequent recognition.
Specifically, DPSS first enhances image contrast and grain-boundary visibility through edge detection and image sharpening. It then combines superpixel segmentation with the extracted edge information to improve boundary localization accuracy while preserving irregular grain morphology, thereby achieving a more complete extraction of grain or particle regions in optical microscopy images. This paper verifies the method on optical microscopy images of Mn-Si low-alloy steel. Experimental results show that, compared with the traditional processing method based on ImageJ (Version 1.54f), DPSS obtains more accurate and complete microstructure segmentation results. On this basis, the paper further introduces a lightweight neural network for phase-structure recognition, ultimately achieving a classification accuracy of 99.91%. This result demonstrates that the improved segmentation method can provide more reliable input for subsequent microstructure recognition. Overall, the proposed method offers an efficient and automated solution for metallographic image segmentation and provides strong support for downstream phase-structure analysis.
Essay: Automatic Segmentation and Recognition of the Microstructure of High-Strength Low-Alloy Steel
