Semantic segmentation model based on edge information for rock structural surface traces detection

Abstract

Fast and accurate detection of rock structural surface traces is crucial for geology and engineering fields. In recent years, deep learning techniques like U-Net (UNet) have been applied to rock structural surface traces detection by virtue of its high accuracy and strong robustness. However, the loss of important information during the downsampling process may hinder the model performance for rock structural surface traces detection. To alleviate this problem, this paper proposes a semantic segmentation model based on edge information (Edge-UNet) for rock structural surface traces detection. In Edge-UNet, an edge pooling method is designed, which can retain more trace features rich in edge information in the downsampling process, so as to enhance the learning of the model for traces. Then, an edge semantic enhancement structure based on edge pooling is designed to strengthen the edge information in Edge-UNet's encoder. In addition, a channel space attention gate based on edge information is incorporated in Edge-UNet's decoder, which facilitates the model to capture fine trace features. These designs clarify the retention and utilization of edge information in principle which enhances the interpretability of the model. Finally, Convolutional neural network -based and Transformer-based semantic segmentation models were selected for comparison experiments with Edge-UNet, respectively. From the experimental results, Edge-UNet outperforms the other models in three performance metrics, which verifies the superior performance of Edge-UNet in rock structural surface trace detection task.