CycleGAN-Based Clutter Suppression and Pipeline Positioning Method for GPR Image

Jiachun Wang;Yun Lin;Deyun Ma;Yanping Wang;Shengbo Ye
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Abstract

The suppression of clutter and the positioning of underground pipelines are crucial steps in the processing of ground-penetrating radar (GPR) data. It is challenging to acquire clutter-free measured data during the radar detection process. As a result, the existing deep learning (DL) methods are primarily trained using simulation data, which limits their applicability to real-world scenarios. To address these challenges, this letter proposes an improved underground clutter suppression and pipeline positioning network. In the first stage, the model is trained using both measured data and simulation clutter-free data to enhance its ability to suppress clutter in measured data. Furthermore, in the second stage, the network is modified to accept paired, labeled simulation data, which enables more accurate pipeline positioning than the original unpaired network. Real-world data evidence demonstrates that the proposed network’s clutter suppression achieves a mean squared error (mse) of 0.006 and a peak signal-to-noise ratio (PSNR) of 34.73 dB. Additionally, the Euclidean distance error of the target clustering center coordinates is 0.82px. Compared to other methods, the performance of the proposed approach has been significantly enhanced.
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基于 CycleGAN 的 GPR 图像杂波抑制和管道定位方法
抑制杂波和定位地下管道是处理探地雷达(GPR)数据的关键步骤。在雷达探测过程中,获取无杂波测量数据具有挑战性。因此,现有的深度学习(DL)方法主要使用模拟数据进行训练,这限制了它们在现实世界场景中的适用性。为了应对这些挑战,本文提出了一种改进的地下杂波抑制和管道定位网络。在第一阶段,使用测量数据和模拟无杂波数据对模型进行训练,以增强其抑制测量数据中杂波的能力。此外,在第二阶段,对网络进行了修改,使其能够接受成对的标注模拟数据,从而使管道定位比原始的非成对网络更加精确。真实世界的数据证明,建议的网络抑制杂波的平均平方误差(mse)为 0.006,峰值信噪比(PSNR)为 34.73 dB。此外,目标聚类中心坐标的欧氏距离误差为 0.82px。与其他方法相比,拟议方法的性能得到了显著提升。
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