面向探地雷达 B-scan图像的目标检测算法综述

利用无损探测技术来获取地下目标的信息是当前研究的热点，探地雷达(GPR)作为一种重要的无损工具，已被广泛用于检测，定位和特征化地下目标。然而，从GPR成像中探测掩埋物体并评估其位置既费时又费力。因此，实现地下目标的自动化探测对实际应用是必要的。为此，该文在综合分析地下目标回波特征的基础上，讨论了使用GPR评估目标位置的可行性，并回顾了国内外学者在GPR成像中对双曲线特征自动化检测的研究进展。该文还在国内外典型实例剖析的基础上，总结并比较了目标检测的处理方法。最后指出，未来的研究应集中于开发新的深度学习检测框架，用以自动检测和估计真实场景中的地下特征。

A Review of Target Detection Algorithm for GPR B-scan Processing

Ground Penetrating Radar (GPR), as a non-destructive technology, has been widely used to detect, locate, and characterize subsurface objects. Example applications include underground utility mapping and bridge deck deterioration assessment. However, manually interpreting the GPR scans to detect buried objects and estimate their positions is time-consuming and labor-intensive. Hence, the automatic detection of targets is necessary for practical application. To this end, this paper discusses the feasibility of using GPR to estimate target positions, and reviews the progress made by domestic and international scholars on automatic hyperbolic signature detection in GPR scans. Thereafter, this paper summarizes and compares the processing methods for target detection. It is concluded that future research should focus on developing deep-learning based method to automatically detect and estimate subsurface features for on-site applications.