In order to improve the accuracy of GPR (ground penetrating radar) B-SCAN hyperbola detection

the deep learning method is applied to the processing of GPR data. In order to solve the problem of insufficient samples in the data set

the GPR B-SCAN image data is augmented by using cycle generative adversarial networks algorithm (CycleGAN). The Faster R-CNN operator is used to locate the hyperbolic image area

making full use of the symmetry of the hyperbolic structure and its directional difference characteristics

designing the corresponding convolution kernel template

and realize effective segmentation of hyperbolic targets in B-SCAN images through convolution op

eration. The least square method is used to perform quadratic curve fitting on the hyperbolic target to obtain an accurate hyperbolic image. Compared with B-SCAN image detection algorithms such as transfer learning-based methods

HOG (histogram of oriented gradients) algorithm and Hough transform algorithm

the results obtained by the method in this paper are optimal on the comprehensive measurement index

F

.