Abstract Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades. Due to rising costs, safety issues and error of human inspection methods, automated methods for bridge inspection and maintenance are being proposed. The purpose of this research is to develop an automated rebar detection and localization system utilizing supervised (Deep Residual Networks) and unsupervised (K- means clustering) techniques. Data has been collected from nine bridges using Ground Penetrating Radar (GPR) sensors. The performance of the proposed rebar detection and localization system has been evaluated on a wide-range of performance metrics, which emphasize the superior performance of the proposed technique over existing methods. The results reveal positive correlation between number of layers of networks, training time and other performance metrics. The overall performance of the proposed system is also dataset-dependent with factors such as noise artefacts, reflections and visual quality of rebar profiles.

中文翻译：

---

使用深度残差网络进行桥面检查和评估的钢筋检测和定位

摘要 在过去的几十年里，民用基础设施的结构健康监测 (SHM) 和无损评估 (NDE) 一直是一个活跃的研究领域。由于成本上升、安全问题和人工检查方法的错误，正在提出用于桥梁检查和维护的自动化方法。本研究的目的是利用监督（深度残差网络）和无监督（K 均值聚类）技术开发自动钢筋检测和定位系统。已使用探地雷达 (GPR) 传感器从九座桥梁收集数据。所提出的钢筋检测和定位系统的性能已经在广泛的性能指标上进行了评估，这些指标强调了所提出的技术优于现有方法的性能。结果表明，网络层数、训练时间和其他性能指标之间存在正相关关系。所提出的系统的整体性能也依赖于数据集，包括噪声伪影、反射和钢筋轮廓的视觉质量等因素。