A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data
Date of Original Version
Manual processing of Ground Penetrating Radar (GPR) images is a very time-intensive task. The authors proposed a novel computer vision-based method for automatic detection of rebars in complex GPR images in highly deteriorated concrete bridge decks. The proposed detection model consists of a fine-tuned Histogram of Oriented Gradients feature descriptor, a Multi-Layer Perceptron for classification, and a post processing algorithm for eliminating false detections and labeling rebar in Region of Interest. State-of-art results are obtained on testing the method on real bridge deck GPR data and comparing the results with RADAN software. Overall accuracy of 89.4% is obtained on URIGPRv1.0 dataset, which is introduced in this paper. The proposed method is 54.35% more accurate comparing to the results obtained by RADAN software. The proposed classifier outperformed accuracy of a 3-layer convolutional neural network by 11.9%.
Publication Title, e.g., Journal
Automation in Construction
Asadi, Pouria, Mayrai Gindy, Marco Alvarez, and Alireza Asadi. "A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data." Automation in Construction 112, (2020). doi: 10.1016/j.autcon.2020.103106.