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

Abstract

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.