Efficient and accurate crack detection can provide a basis for assessing the structural safety of tunnels. Aiming at the shortcomings of traditional crack detection methods, which are complex and weak in generalization ability, an improved algorithm YOLOv5-CT(YOLOv5 CBAM Transformer) for tunnel lining crack detection was proposed. Considering the slender morphology of the cracks, the network introduced the Transformer module to improve the crack detection effect. The strong long-range dependency capture ability of the Transformer module enabled the proposed detection model to fully learn the contextual information of the crack region. In addition, the network integrated the convolutional attention mechanism CBAM(convolutional block attention module) in neck. The experiment shows that the YOLOv5-CT can achieve AP50 and AP of 85.2% and 51.3%, respectively, which is an improvement of 8.9% and 12.1% compared to the baseline model YOLOv5. It is better than other one-stage object detection networks in terms of accuracy, and the inference speed reaches 161.3 fps under 640 × 640 pixel conditions, which meets real-time detection of tunnel lining cracks.