Tunnel Lining Defect Identification Method Based on Small Sample Learning

Abstract

Aiming at the problem of insufficient number of samples due to the difficulty of data acquisition in the identification of tunnel lining defects, a generative adversarial network was introduced to expand the data, and the network was improved for the mode collapse problem of the traditional generative adversarial network and the problem that the generated image features were not obvious. On the basis of the WGAN-GP network, a deep convolutional network is selected as its backbone network, and the effectiveness of the deep convolutional network in feature extraction by Lv et al. (2022) is used to improve the quality of the images generated by the network. In addition, the residual module is introduced into the discriminator network, and the upsampling module is introduced into the generator network, which further solves the problem of gradient disappearance of the two networks during the update iteration process through the idea of cross-connection, while better retaining the underlying features, which effectively solves the problem of mode collapse and low quality of generated images in the generative adversarial network. Compared with the original network, the image quality of the generated adversarial network is improved, and the discriminator and generator losses converge faster. At the same time, the recognition accuracy of the YOLOv5 network is improved by 4.4% and the overfitting phenomenon is alleviated, which proves the effectiveness of the method under the limited training data set.