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Aiming at the problems of inefficient manual detection of tunnel cracks, inconvenient maintenance, complicated and changeable tunnel environment, and susceptibility to noise interference, a crack detection algorithm based on deep learning is proposed. Non-crack areas are filtered through the neural network to reduce the interference of irrelevant background information. At the same time, based on the segmentation algorithm, mis-recognized crack areas are eliminated. Experimental results show that the DenseNet network can reach a maximum accuracy of 99.95% in crack classification, which effectively improves the accuracy of tunnel crack detection.