Tunnel Lining Voids Detection Method Incorporating Guide Anchor Mechanism

摘要

In the process of detection of tunnel voids by Ground Penetrating Radar(GPR), the shape of void disease is complex, data analysis depends on artificial recognition and other issues. This paper constructs a convolution neural network which integrates the mechanism of guiding anchoring to detect tunnel voids. The network consists of four parts: feature extraction, recommendation box generation of anchor area, pooling of interested area and classification regression: feature extraction network to extract disease features of the rich samples; guide anchor area recommendation network to join the GIoU evaluation standard, and predict the anchor shape through learning; the feature maps obtained are clustered after the region of interest. Finally, the disease features are classified and the boundary box regression is carried out. Compared with the existing target detection algorithm, the experimental results show that the improved network achieves 92.61% classification accuracy, and the trained model has good generalization ability and robustness.