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 structure, CNN named Segnet coupled with Lovász softmax loss function, which enhances accuracy, automation, and efficiency identification. Experiments both actual data show that our innovative method overcomes problems in standard interpretation. A physical test model known was developed manufactured, acquired analyzed to verify approach.