Simulation of GPR B-Scan Data Based on Dense Generative Adversarial Network
Simulation of GPR B-Scan Data Based on Dense Generative Adversarial Network
复制标题
DOI：
10.1109/jstars.2023.3267482
复制
发表时间：
2023
影响因子：
5.5
通讯作者：
Bin Wang;Peiyao Chen;Gong Zhang
中科院分区：
文献类型：
--
作者：
Bin Wang;Peiyao Chen;Gong Zhang
随着现代城市的扩张，城市地下基础设施，如管道和道路，正在老化。由于具有无损探测的特点，探地雷达在地下目标或灾害探测中得到了广泛的应用，而探地雷达b扫描图像多采用人工解译。这种具有高度主观性和不确定性的方法不可避免地导致检测失败。同时，标记图像的缺乏极大地阻碍了探地雷达地下灾害探测的自动化和智能化。许多数据模拟技术，如正演建模，被用来增强图像进行训练；然而，生成的前向图像与真实的b扫描数据不够相似，这使得识别成为一项具有挑战性的任务。为了解决这个问题，我们提出了一种新的基于生成对抗网络的b扫描图像仿真方法，以生成用于训练检测网络的合成图像。我们的网络使用DenseNet作为生成器的主干网络来提取图像特征，并使用加权总变分正则化项来正则化网络的损失函数。对比和烧蚀实验验证了我们的网络能够生成与GPR b扫描真实图像具有较高相似度的模拟图像。我们认为，这项工作有助于探地雷达数据的智能处理和分析，提高地下灾害探测的效率。
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.