Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

Symmetry - Tập 10 Số 4 - Trang 107
Boyuan Ma1,2, Xiaojuan Ban1,2, Haiyou Huang3, Chen Yu-lian1,2, Wanbo Liu3, Yonghong Zhi4
1Beijing Key Laboratory of Knowledge Engineering for Materials Science, Xueyuan Road 30, Haidian District, Beijing 100083, China
2School of Computer and Communication Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
3Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
4Mechanical and Electrical Design and Research Institute of Shanxi Province, Shengli Street 228, Xinghualing District, Taiyuan 030009, Shanxi, China

Tóm tắt

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.

Từ khóa


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