Multiscale Convolutional Generative Adversarial Network for Anchorage Grout Defect Detection

Grout is an important part of the bolt anchorage system; once having the damage, the cohesive force will not reach the requirement, which may affect the support effect. Moreover, as buried in the rock, the defects that happened in the grout have unknown characteristics. In order to identify the grout defect, a multiscale convolutional generative adversarial network (MSCGAN) method is proposed in this article. The generator based on supervised learning is designed in the MSCGAN framework, which can generate data for enriching the diversity of the system characteristics. Together with adopting the convolutional neural network, which can realize end-to-end learning as a discriminator, MSCGAN also draws on the idea of multiscale to improve the ability of feature extraction. The proposed method was experimentally verified on the bolt anchorage system experimental platform. Through the comprehensive analysis of the experimental results of different methods, the effectiveness of the proposed method is demonstrated.