Abstract Ground penetrating radar (GPR) has been widely used for detection and localization of reinforced steel bar (rebar) in concrete in a non-destructive way. However, manual interpretation of a large number of GPR images is time-consuming, and the results highly depend on practitioner experience and the available priori information. This paper proposes an automatic detection and localization method using deep learning and migration. Firstly, a Single Shot Multibox Detector (SSD) model is established to identify regions of interest containing hyperbolas in a GPR image. This deep learning model is trained using a real GPR dataset, which contains 13,026 rebar targets in 3992 images, collected on residential buildings under construction. Secondly, each target region is migrated and transformed into a binary image to locate the rebar. After the binarization, the apex of the focused cluster is obtained and used to estimate both the horizontal position and the depth of the rebar. The testing results show that the detection accuracy of the proposed artificial intelligence method is 90.9%. The computation time needed for processing a GPR image with a size of 300 × 300 pixels is only 0.47 s. The depth estimation error in a laboratory experiment is

中文翻译：

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基于探地雷达深度学习的混凝土钢筋检测与定位

摘要 探地雷达(GPR)已广泛应用于混凝土中钢筋（钢筋）的无损检测和定位。然而，对大量 GPR 图像的人工解释非常耗时，而且结果在很大程度上取决于从业者的经验和可用的先验信息。本文提出了一种使用深度学习和迁移的自动检测和定位方法。首先，建立单次多盒检测器（SSD）模型来识别 GPR 图像中包含双曲线的感兴趣区域。该深度学习模型是使用真实的 GPR 数据集进行训练的，该数据集包含 3992 幅图像中的 13,026 个钢筋目标，这些图像是在在建住宅建筑上收集的。其次，将每个目标区域迁移并转换为二值图像以定位钢筋。在二值化之后，获得聚焦簇的顶点并用于估计钢筋的水平位置和深度。测试结果表明，所提出的人工智能方法的检测准确率为90.9%。处理尺寸为 300 × 300 像素的 GPR 图像所需的计算时间仅为 0.47 s。实验室实验中的深度估计误差为