Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images
Boyuan Ma,
Xiaojuan Ban,
Haiyou Huang,
Yulian Chen,
Wanbo Liu,
Yonghong Zhi
Affiliations
Boyuan Ma
School of Computer and Communication Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
Xiaojuan Ban
School of Computer and Communication Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
Haiyou Huang
Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
Yulian Chen
School of Computer and Communication Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
Wanbo Liu
Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
Yonghong Zhi
Mechanical and Electrical Design and Research Institute of Shanxi Province, Shengli Street 228, Xinghualing District, Taiyuan 030009, Shanxi, China
Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes three key contributions. (1) We train a deep convolutional neural network based on DeepLab to achieve image segmentation and have significant results. (2) We adopt a local processing method based on symmetric overlap-tile strategy which makes it possible to analyze the microscopic images with high resolution. Additionally, it achieves seamless segmentation. (3) We apply symmetric rectification to enhance the accuracy of results with 3D information. Experimental results showed that our method outperforms existing segmentation methods.
