Abstract: To improve the detection accuracy and efficiency of cracks during regular tunnel inspections, this study proposes an FC-ResNet algorithm for tunnel lining crack detection by using ResNet as the backbone feature extraction network, incorporating U-net's "encoder-decoder" structure and optimizing network feature layers. The algorithm achieves pixel-level segmentation of lining cracks. To verify its effectiveness and reliability, a comparative validation was conducted using CrackSegNet and U-net. The results show that the proposed algorithm demonstrates excellent detection performance, with a pixel accuracy, mean Intersection over Union (mIoU), and F1-score of 99.2%, 87.4%, and 0.87, respectively, on the test set. These results are superior to those of CrackSegNet and U-net,and the detection time per image is 122 ms, better than CrackSegNet and comparable to the simpler U-net. Based on the FC-ResNet algorithm, an intelligent recognition system for tunnel lining cracks was developed, enabling accurate and fast intelligent recognition of cracks in actual tunnel engineering linings.