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With the rapid development of urban subways in our country, the maintenance of tunnels has become more and more important. The traditional manual inspection methods are not only low in efficiency, high in cost, but also time-consuming, which can no longer meet today's needs. By studying the disease characteristics of the cable channel in the cross-river tunnel, a method for detecting multiple diseases in the tunnel based on deep learning is proposed, and a residual fusion module network (Resfmnet) for the detection of tunnel diseases is proposed, using deep learning The network extracts image disease features and performs disease classification, which improves the detection ability of diseases. The data set used is obtained from the video taken by special robots in the cable channel of the cross-river tunnel; The experimental results show that the proposed network shows higher accuracy and generalization, and the accuracy mAP of multi-disease detection reaches 0.8914, which makes the inspection and monitoring of the cross-river tunnel more efficient and low-cost, and finally realizes automation.