高效、准确的衬砌裂缝检测可以为评估隧道结构安全提供依据。针对传统裂缝检测方法复杂和泛化能力弱的缺点，提出了一种基于深度学习的隧道衬砌裂缝检测网络YOLOv5-CT（YOLOv5 CBAM Transformer）。考虑到裂缝细长的形态，网络引入了Transformer模块来改善裂缝检测效果。Transformer模块较强的长距离依赖捕捉能力使得所提出的检测模型能够充分学习到裂缝区域的上下文信息。此外，该网络在特征融合部分还集成了卷积注意力机制CBAM（convolutional block attention module）。在自采集数据集上的实验结果表明：YOLOv5-CT的AP50和AP（average precision）分别可以达到85.2%和51.3%，相比于基线模型YOLOv5提高了8.9%和12.1%，在精度上优于YOLOX、YOLOv3-MobileNet等其他单阶段目标检测网络。在640 × 640像素条件下推理速度达到161.3 f/s（frames per second），可以实现隧道衬砌裂缝实时检测。

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.