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- W3003588722 abstract "Abstract 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%." @default.
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- W3003588722 date "2020-04-01" @default.
- W3003588722 modified "2023-09-23" @default.
- W3003588722 title "A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data" @default.
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- W3003588722 doi "https://doi.org/10.1016/j.autcon.2020.103106" @default.
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