GCNet: Ground Collapse Prediction Based on the Ground-Penetrating Radar and Deep Learning Technique

Abstract

Manual methods to detect and identify potential ground collapse hazards have a high volume of workload and rely on manual skills and experience strongly, with substantial inconsistencies. This paper proposes an automatic recognition method of potential underground hazards and designs an object detection-based algorithm to predict the hidden danger of ground collapse, called GCNet. The GCNet is a ground collapse hazard detection network, which is developed to detect underground hazards, including cavities, poor soil quality, pipelines, and soil background. The GCNet uses the deep residual network ResNet101 and feature pyramid network (FPN) to extract features and a task coordination network (TCN) to identify the category and location of hidden underground hazards. Further, a feature enhancement method based on the regional binary pattern is proposed to improve the accuracy of the proposed model by expanding the ground-penetrating radar (GPR) data by adding multi-level features to GPR images. The experiments are conducted on a large amount of real GPR data and experiment results show that the proposed automatic recognition method can surpass the existing deep learning-based methods in hidden underground hazard recognition and identification.