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Convolution neural network based on fusion parallel multiscale features for segmenting fractures in coal-rock images

机译:基于融合并行多尺度特征的卷积神经网络,用于煤岩图像分割骨折

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摘要

The coal-rock fractures formed by natural geological evolution are complex and the shapes are not fixed, which makes it difficult to manually define the features of coal-rock fractures. Based on the full convolutional neural network U-net, we propose a convolutional neural network with fusion parallel multiscale features (FPMF-U-net). The FPMF-U-net uses two feature extraction networks in parallel to extract multiscale features. The feature fusion layer in this network is responsible for weighted fusion of the shallow high-resolution features and the deep abstract features. Average pooling operator is used in the network to avoid loss of weak boundary fractures caused by max pooling. The FPMF-U-net can automatically extract fracture features and segment coal-rock fractures from images. According to the three-dimensional gradual change feature of the fracture shapes between adjacent images, the segmentation results of the FPMF-U-net are further optimized Due to the lack of training samples of the fracture, we use a data augmentation technique to increase the number of training samples. At the same time, the transfer learning method is used to improve the convergence speed of the FPMF-U-net. The experimental results show that the proposed FPMF-U-net has a good effect on the segmentation of coal-rock fractures. (C) 2020 SPIE and IS&T
机译:由天然地质演化形成的煤岩骨折是复杂的,并且形状不固定,这使得难以手动地定义煤岩骨折的特征。基于完整的卷积神经网络U-Net,我们提出了一种具有融合并行多尺度特征(FPMF-U-Net)的卷积神经网络。 FPMF-U-Net使用两个特征提取网络并行以提取多尺度特征。该网络中的特征融合层负责浅层高分辨率功能的加权融合和深度抽象特征。网络中使用平均池操作员,以避免Max池引起的弱边界骨折损失。 FPMF-U-净可以自动从图像中提取骨折特征和分段煤岩骨折。根据相邻图像之间的断裂形状的三维渐变特征,由于缺乏骨折训练样本,FPMF-U-Net的分段结果进一步优化,我们使用数据增强技术来增加训练样本数量。同时,转移学习方法用于提高FPMF-U-Net的收敛速度。实验结果表明,所提出的FPMF-U-Net对煤岩骨折的分割具有良好的影响。 (c)2020个SPIE和IS&T

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