Affiliations: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
Correspondence:
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Corresponding author. Guoying Zhang, School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Ding No. 11 Xueyuan Road, Haidian District, Beijing, China, 100083. Tel.: +86 137 1755 9517; E-mail: zhangguoying1101@163.com.
Abstract: Accurate segmentation of fractures in coal rock CT images is important for the development of coalbed methane. However, due to the large variation of fracture scale and the similarity of gray values between weak fractures and the surrounding matrix, it remains a challenging task. And there is no published dataset of coal rock, which make the task even harder. In this paper, a novel adaptive multi-scale feature fusion method based on U-net (AMSFF-U-net) is proposed for fracture segmentation in coal rock CT images. Specifically, encoder and decoder path consist of residual blocks (ReBlock), respectively. The attention skip concatenation (ASC) module is proposed to capture more representative and distinguishing features by combining the high-level and low-level features of adjacent layers. The adaptive multi-scale feature fusion (AMSFF) module is presented to adaptively fuse different scale feature maps of encoder path; it can effectively capture rich multi-scale features. In response to the lack of coal rock fractures training data, we applied a set of comprehensive data augmentation operations to increase the diversity of training samples. These extensive experiments are conducted via seven state-of-the-art methods (i.e., FCEM, U-net, Res-Unet, Unet++, MSN-Net, WRAU-Net and ours). The experiment results demonstrate that the proposed AMSFF-U-net can achieve better segmentation performance in our works, particularly for weak fractures and tiny scale fractures.
Keywords: Multi-scale feature fusion, U-net, fracture segmentation in coal rock CT image, dilation convolutions, residual U-net
