Unsupervised learning method for rebar signal suppression and defect signal reconstruction and detection in ground penetrating radar images

The signals of reinforcing steel bars (rebars) in ground penetrating radar (GPR) images may mask defect signals underneath them, negatively affecting defect detection in reinforced concrete. This study proposes an unsu-pervised learning-based method trained using unpaired GPR images to suppress the rebar signal and reconstruct the defect signal on the recorded GPR B-Scan images. In particular, four contrastive feature encoders and two similarity feature encoders are designed in the network. The extracted features of contrastive feature encoders and similarity feature encoders are constrained by contrastive and similarity loss functions, respectively. The proposed method was validated using data from three scenarios: synthetic data, sandbox experimental data, and data collected from reinforced concrete in field. The results showed that the proposed method outperforms other unsupervised methods, and it can effectively suppress the rebar signals and accurately reconstruct the defect signals. Moreover, the identification accuracy of defect underneath rebars can be improved significantly.