Rebar Detection using Ground Penetrating Radar with State-of-the-art Convolutional Neural Networks Habib Ahmed 1 , Hung Manh La 1 , and Nenad Gucunski 2 1 Advanced Robotics and Automation (ARA) Lab, Department of Computer Science and Engineering, University of Nevada, Reno, NV89557, USA. Corresponding Author’s Email: hla@unr.edu. 2 Department of Civil and Environmental Engineering, Rutgers, the State of University of New Jersey – Piscataway, NJ08854, USA. Abstract Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades, since traditional inspection of civil infrastructure, mostly relying on visual inspection, is time-consuming, labor-intensive and often provides subjective results. To facilitate this process, different sensors and techniques have been used to effectively carry out this task in an automated fashion. The purpose of this research is to provide a novel learning-based method for detection of steel rebars in reinforced concrete bridge elements. The data from Ground Penetrating Radar (GPR) collected from actual bridges has been used for the development and testing of the novel method. The method utilizes one of the variants of the Convolution Neural Networks (CNNs), namely the Deep Residual Network (ResNet-50). The findings are highlighted through a comparison of the effectiveness in rebar detection with some of the recent works. The results further emphasize the efficacy of Deep Learning-based methods for NDE, both in general and in rebar detection by GPR. 1. Introduction A modern society’s infrastructure comprises mostly of concrete and steel structures, ranging from houses and buildings to transportation infrastructure, such as tunnels, roadways and bridges. The safety of human inhabitants and users is dependent on the regular inspection and evaluation of the civil infrastructure. For the case of transportation infrastructure monitoring, a number of NDE techniques have been explored and implemented in the past. Some of the most commonly used NDE methods include: (i) impact-echo (uses mechanically generated vibrations and their reflection to detect sub-surface defects), active and passive infrared thermography (provides defect detection using electromagnetic radiations that vary with temperature) and ground penetrating radar (the transmission and reception of electromagnetic radio waves to detect underground defects) (Wang et al., 2011; Kee et al., 2012; La et al., 2015; Kaur et al., 2016). Each method has its own set of benefits and limitations. Traditionally, assessment of civil infrastructures has been performed manually by human inspectors, relying primarily on visual inspection (Wang et al., 2011). However, visual inspection work is time-consuming and prone to errors. Even the NDE methods, like GPR, require extensive effort to manually process raw data and extract relevant information (Dinh et al., 2018). For this reason, an effort is being made towards introducing automation within the process of civil infrastructure assessment by developing new systems, which can facilitate rapid and automated detection and localization of cracks, and other forms of deterioration and defects.