Deep Learning-Based Rebar Clutters Removal and Defect Echoes Enhancement in GPR Images

The clutters of rebar in the ground penetrating radar (GPR) images may mask the echoes of the inner defects under the rebars, which adversely affects the identification of the inner structural defects in the reinforced concrete (RC). In this study, a deep learning-based method for rebar clutters removal and defect echoes enhancement in GPR B-scan images was proposed. The residual-inception blocks and attention modules were designed in the network as per the characteristics of the task. Validation of the method has been performed at three levels: first using synthetic data, next by synthetic data superimposed with actual concrete background noise, and finally using a sandbox model test in a realistic scenario. The results indicate that the proposed method had the ability to effectively remove rebar clutters and reconstruct complete defect echoes, with performance superior to those of the traditional deep-learning based methods. A RC structural defect identification experiment was performed to verify the effectiveness of the proposed method. The results indicated that after the rebar signals were removed using the method proposed, the accuracy for identifying the defects under the rebars had been improved from 0.208 to 0.850. The proposed method is promising for applying to practical engineering due to its fast processing and better performance.

Automated summary

This study proposes a deep learning-based method for removing rebar clutters and enhancing defect echoes in ground penetrating radar (GPR) images. The method effectively reconstructs complete defect echoes and improves the accuracy for identifying defects under the rebars, showcasing promising application potential in practical engineering due to its fast processing and superior performance.