Ground Penetrating Radar (GPR) has been widely used to estimate the healthy operation of some urban roads and underground facilities. When identifying subsurface anomalies by GPR in an area, the obtained data could be unbalanced, and the numbers and types of possible underground anomalies could not be acknowledged in advance. In this paper, a novel method is proposed to improve the subsurface anomaly detection from GPR B-scan images. A normal (i.e. without subsurface objects) GPR image section is firstly collected in the detected area. Concerning that the GPR image is essentially the representation of electromagnetic (EM) wave and propagation time, and to preserve both the subsurface background and objects' details, the normal GPR image is segmented and then fused with simulated GPR images that contain different kinds of objects to generate the synthetic data for the detection area based on the wavelet decompositions. Pre-trained CNNs could then be fine-tuned with the synthetic data, and utilized to extract features of segmented GPR images subsequently obtained in the detection area. The extracted features could be classified by the one-class learning algorithm in the feature space without pre-set anomaly types or numbers. The conducted experiments demonstrate that fine-tuning the pre-trained CNN with the proposed synthetic data could effectively improve the feature extraction of the network for the objects in the detection area. Besides, the proposed method requires only a section of normal data that could be easily obtained in the detection area, and could also meet the timeliness requirements in practical applications.

提出了一种基于预训练卷积神经网络的综合数据和一分类学习算法的地下物体检测方法。该方法通过采集探测区域的正常GPR图像和模拟包含不同物体类型的GPR图像来生成综合数据，用于微调卷积神经网络，进而提取探测区域内的地下物体特征并进行分类，无需预先设置地下物体类型和数量。实验表明，该方法能够有效提高卷积神经网络对探测区域内物体的特征提取能力，并且满足实际应用中的及时性要求。

使用合成数据微调CNN，提高GPR图像的异常检测能力