A denoising method for underground pipeline data acquired by ground penetrating radar based on DnNet

doi:10.11947/j.AGCS.2025.20230508

Abstract

Abstract:

The presence of noise can seriously affect the intelligent interpretation and identification of ground-penetrating radar (GPR) underground pipelines. In view of this, this paper proposes a PnNet-based denoising method for GPR under ground pipeline data. Among them, this algorithm constructs a brand-new deep learning denoising network by using the encoder-decoder structure, group normalization and simplified channel attention mechanism, achieving a significant improvement in the denoising performance of ground penetrating radar images. The feedforward network is improved by using deep convolutional blocks, effectively enhancing the network's ability to recover waveform edge information. Meanwhile, due to the simplification of the channel attention mechanism and the improvement of the feedforward network, the noise reduction efficiency has been significantly enhanced. The experimental results show that the proposed algorithm has a good noise reduction effect. In the noise reduction of simulated GPR images, compared with the dictionary learning method, Cycle GAN, DRUNet and DnCNN, when the standard deviation of noise is equal to 50, the peak signal-to-noise ratio of the proposed algorithm has increased by 24.72, 24.3, 23.54 and 23.86 dB respectively. The structural similarities have increased by 0.545 5, 0.424 2, 0.140 8 and 0.375 9 respectively. In the actual denoising of GPR data, the proposed algorithm can remove most of the noise and retain the waveform details of underground pipelines compared with other algorithms.

Key words: GPR image denoising, encoder-decoder, group normalization, channel attention

CLC Number:

P237

Huiqin WANG, Jiahao LI, Xin LIU, Yongqiang HE, Jia LUO, Bincan LIU. A denoising method for underground pipeline data acquired by ground penetrating radar based on DnNet[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(8): 1464-1475.

Figures/Tables 10

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深度模型	Depth	Width	Small	Middle	Large
编码器深度	[2，2，4，8]	[2，2，2，2]	[2，2，2，2]	[2，2，4，8]	[4，6，6，8]
瓶颈层深度	12	24	12	24	24
解码器深度	[2，2，2，2]	[2，2，2，2]	[2，2，2，2]	[2，2，2，2]	[2，2，2，2]
训练时间/h	11.5	17.8	13.5	20.9	25.1

深度学习算法	DRUNet	DnCNN	DnNet
计算复杂度MACs	2.0×10¹⁰	1.7×10¹⁰	2.2×10¹⁰
推断速度/s	1.53	0.79	1.74