The ground penetrating radar response recognition of hidden dam defects using multi-output convolutional neural network

The Ground Penetrating Radar (GPR) data interpretation of dam defects detection by manual process is a heavy task. Although the convolutional neural network (CNN) is applicable to detect road defects with the help of other auxiliary instruments and techniques, it is still a challenge for the dam defect detection by deep learning. To overcome this problem, a multi-output CNN model for GPR response recognition of dam defects is proposed. The training dataset and test dataset are produced from a large number of GPR responses with different types of dam defects forward modeling by GPRMax3.0 software. The results show that the highest recognition accuracy of the training dataset and test dataset is about 98% and 97%, respectively. To verify the effectiveness, the new proposed method is used to recognize the hidden GPR responses of dam defects in the real GPR data. The results show that the types and locations of dam defects in the real GPR data can be recognized and classified automatically, efficiently and accurately.