Automated GPR Rebar Analysis for Robotic Bridge Deck Evaluation

作者：Kaur Parneet^*; Dana Kristin J; Romero Francisco A; Gucunski Nenad

来源：IEEE Transactions on Cybernetics, 2016, 46(10): 2265-2276.

DOI：10.1109/TCYB.2015.2474747

• Automatic rebar detection
• depth correction
• ground penetrating radar (GPR)
• histogram of oriented gradients (HOG)
• hyperbolic signature
• machine learning
• pattern recognition
• robotic bridge inspection
• robust curve fitting
• support vector machines (SVM)

Ground penetrating radar (GPR) is used to evaluate deterioration of reinforced concrete bridge decks based on measuring signal attenuation from embedded rebar. The existing methods for obtaining deterioration maps from GPR data often require manual interaction and offsite processing. In this paper, a novel algorithm is presented for automated rebar detection and analysis. We test the process with comprehensive measurements obtained using a novel state-of-the-art robotic bridge inspection system equipped with GPR sensors. The algorithm achieves robust performance by integrating machine learning classification using image-based gradient features and robust curve fitting of the rebar hyperbolic signature. The approach avoids edge detection, thresholding, and template matching that require manual tuning and are known to perform poorly in the presence of noise and outliers. The detected hyperbolic signatures of rebars within the bridge deck are used to generate deterioration maps of the bridge deck. The results of the rebar region detector are compared quantitatively with several methods of image-based classification and a significant performance advantage is demonstrated. High rates of accuracy are reported on real data that includes thousands of individual hyperbolic rebar signatures from three real bridge decks.

• 出版日期2016-10