Simulation of GPR B-Scan Data Based on Dense Generative Adversarial Network

Urban subsurface infrastructures, e.g., pipelines and roads, are aging with the expansion of modern cities. Benefiting from the capability of nondestructive detection, ground penetrating radar (GPR) has been widely applied to underground objects or disasters detection, and GPR B-scan images are employed by manual interpretation. This way of high subjectivity and uncertainty inevitably results in failure of detection. Meanwhile, the shortage of labeled images greatly impedes the automatization and intelligentization of underground disaster detection based on GPR. Many data simulation techniques, e.g., forward modeling, were used to augment images for training; however, the generated forward images were not similar enough to the real B-scan data, which makes recognition a challenging task. To address this problem, we proposed a novel B-scan image simulation method based on a generative adversarial network to generate synthetic images for training detection networks. Our network utilizes DenseNet as the backbone network of the generator to extract image features, and a weighted total variation regularization term to regularize the loss function of the network. The comparison and ablation experiments verified that our network could generate simulation images with high similarity to real GPR B-scan images. We believe that this work contributes to the intelligent processing and analysis of GPR data and improves the efficiency of underground disaster detection.