Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network

This work offers a defect segmentation approach for the nondestructive testing of tunnel lining internal defects using Ground Penetrating Radar (GPR) data. Given GPR synthetic data, it maps the internal defect structure, using a CNN named Segnet coupled with the Lovasz softmax loss function, which enhances the accuracy, automation, and efficiency of defect identification. Experiments with both synthetic and actual data show that our innovative method overcomes problems in standard GPR data interpretation. A physical test model with a known defect was developed and manufactured, and GPR data was acquired and analyzed to verify the approach.