A Shearlet Transform-Based Method for Automated Rebar Recognition Using GPR B-Scan Image Data
Publication: Construction Research Congress 2022
ABSTRACT
Researchers have developed various automated methods to recognize rebar using ground penetrating radar (GPR). These methods can be classified as either pattern-based or machine-learning-based methods. However, these methods are limited by their low robustness in noisy cases or heavy dependence on training large-size datasets. Thus, this paper proposes a rebar recognition method based on the ShearLet Transform, which can distinguish signal from different scales and directions. The proposed method includes two sequential phases: (1) hyperbola decomposition that decomposes rebar signal into a specific scale and direction; and (2) hyperbola reconstruction that reassigns the decomposition components to form new hyperbola without noise. A concrete building is selected to validate our method. The results revealed that: (1) the proposed method can achieve F1 score with 0.9649 on the collected dataset; and (2) it is a robust method that can discriminate strong noise, separate interlaced rebars, and remove cross rebar signals and direct wave.
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Construction Research Congress 2022
Pages: 30 - 39
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Published online: Mar 7, 2022
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