Context-aware semantic segmentation network for tunnel face feature identification

Abstract

The automated interpretation of tunnel face geological information is significant to the construction decision-making of rock mass engineering. An intelligent recognition algorithm, named the Transformer and Convolution neural networks Semantic segmentation Network (TCSeNet), is introduced to overcome the low interpretation accuracy caused by certain limitations of existing automated interpretation methods. Firstly, a hybrid encoder is constructed to extract global and local context information using Transformer and Convolution Neural Networks, respectively. Furthermore, a context feature adaptive selection module is designed to cross-fusion the information. Secondly, a feature pyramid-like decoder is constructed for multi-scale object problems. Furthermore, a multi-scale feature refinement module is designed to improve the ability of multi-scale feature expression. A tunnel face dataset is constructed for training and testing. The research shows that the mIoU of TCSeNet reaches 92.57%. This indicates that the TCSeNet is suitable for automatic interpretation of tunnel face geological information.