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Aiming at low accuracy of recognition in complex leakage images of subway tunnels, this paper studied the application of image recognition algorithms in tunnel water-leakage disease, was using two kinds of neural networks: U-Net and PSP-Net. Based on the subway tunnel images, tunnel leakage disease sample data set was constructed, where 5000 subway tunnel disease image data and 4000 labeled images were included. This data set could be used for deep neural convolution network training. U-Net and PSP-Net deep learning frameworks were established, and the neural networks were trained by self-made data set. The accuracy of the prediction results from U-Net and PSP-Net was evaluated, and the performance of the two networks in the application of leakage disease identification was compared. U-Net had stronger coverage for the disease and quick detection, while PSP-net had higher accuracy fit to fine detection. [ABSTRACT FROM AUTHOR]