Water leakage is one of the most hazardous diseases in shield tunnels,and the rapid and accurate detection of leakage water in shield tunnels is the basis for effective control and rectification of shield tunnel leakage.The existing leakage water detection methods have achieved certain results in terms of automation,but there are problems such as low data collection efficiency,high on site collection environment requirements,and large sample size of training data.To address the above problems,a water leakage detection method for shield tunnels based on generative adversarial network is proposed,which takes the intensity images of shield tunnels collected by mobile LiDAR as the data source.Firstly,starting from the existing V GAN model of the generative adversarial network,based on labelling a small number of samples,the Dense block as encoder is established.Secondly,the Unet model with residual block as decoder is used as a generator network.Finally,the Improved Deep Residual Unet(Improve ResUnet)is applied as a discriminator network to form a DRUnet IRUnet GAN generative adversarial network for water leakage detection of LiDAR intensity images of shield tunnels.The experimental results show that the DRUnet IRUnet GAN generative adversarial network constructed in this paper can achieve the leakage water detection effect of shield tunnel strength image better than V GAN when a small number of samples are of 500,200 and 100 are inputted,indicating that the improved network has good performance.