Detection and localization of rebar in concrete by deep learning using ground penetrating radar

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 <1.5 mm (5%), and the lateral position error is <0.7 cm. Therefore, it is concluded that the proposed method can automatically detect the rebar from GPR images in real time when a handheld GPR system is operated at a walking speed and the depth estimation accuracy is acceptable in practice.