A deep learning-based algorithm for intelligent prediction of adverse geologic bodies in tunnels

Abstract

Aiming at the issues of high subjectivity and low efficiency in the image analysis methods for overcast prediction of tunnel adverse geological bodies, a deep learning-based intelligent prediction algorithm, namely YOLO-SEI (YOLOv8 enhanced by Sim-EFFcinetNet and Interlaced Sparse Self-Attention), is proposed in this paper. Firstly, Sim-EfficientNet with good feature extraction performance and efficiency is proposed as the backbone of YOLOv8 by fusing the SimAM attention and the EfficientNet-v2 network, which improves the model's extraction capability for radar wave features of adverse geologic bodies. Then, a feature fusion module enhanced by Interlaced Sparse Self-Attention is designed to effectively make up for the deficiency of convolutional neural network that is difficult to fully extract the global information of radar images. The experimental results show that the mAP and F1 of YOLO-SEI are 84.87% and 82.28%, respectively, which are higher than other commonly used deep learning models. In addition, YOLO-SEI has smaller storage space (41MB) and faster image processing speed (41.24 f/s), which is suitble for the rapid measurement and prediction of adverse geologic bodies in tunnel excavation construction.