The invention discloses a
ground penetrating radar data generation method and device based on a road material constraint adversarial neural network, equipment and a medium, and relates to the technical field of road detection, and the method comprises the steps: obtaining
ground penetrating radar B-SCAN
original data and road material parameters of the same road detection area, carrying out the
standardization of the
original data, carrying out the channel splicing, obtaining a multi-
modal feature, and carrying out the recognition of the B-SCAN
original data and the road material parameters; combining
noise and geological constraints to construct joint input features; single-
channel data is generated step by step through a three-stage hierarchical generator, a generated sample and a real sample are constructed, and confrontation training is carried out through a two-dimensional
discriminator until convergence to obtain a target generator; and the B-SCAN data of the target
ground penetrating radar can be output by inputting the data to be detected. According to the method, multi-
modal geological constraint and layered generation are realized, data are more real and reasonable, the problems of insufficient samples and generation
distortion are effectively solved, richer ground penetrating
radar B-SCAN data are provided for a road damage area, and the intelligent detection level of road internal damage can be improved.