隧道衬砌检测是隧道建造与维护的一项重要质量管理内容。因衬砌内部病害种类多样、边界模糊，导致病害识别不准、模型训练困难。而依靠人工识别或现有模型无法实现快速、准确得检测病害。针对上述问题，以云南隧道B-scan取得的1 922个衬砌雷达样本创建数据集，用于训练模型，并提出一种基于YOLOv5的隧道衬砌病害检测模型YOLO-Tunnel。通过改进Backbone与Neck，提升了模型特征提取能力、增加感受野、提升模型定位能力；根据数据集规模和目标尺寸比例，选择合适的模型尺寸和平衡权重，进一步提高模型识别能力。结果表明：YOLO-Tunnel相比YOLOv5s有更好的病害检测精度同时也满足实时检测需求，其中准确率、召回率、均值平均精度（mAP）分别提高了2.5、9.0、8.1个百分点，推理时间增加了2.7 ms，为21.8 ms。研究成果为进一步提高隧道衬砌病害识别性能及模型优化方向提供参考。

Tunnel lining detection is an important element of quality management in tunnel construction and maintenance. Due to the variety of internal lining defects and unclear boundaries makes it challenging to identify these problems and train models effectively. Relying on manual detection or existing models, it is not possible to achieve fast and accurate defect detection. To address the above problems, A dataset consisted of 1 922 liner radar samples collected from Yunnan Tunnel B-scan was developed for training the model. A tunnel lining defect detection model YOLO-Tunnel based on YOLOv5 was proposed, which improved the model feature extraction ability, increased the receptive field, and improved the model localization ability by upgraded the Backbone and Neck. And further improved the model detection ability by selected the appropriate model size and balanced weight based on the dataset's scale and target size proportions. The results show that YOLO-Tunnel has better defect detection accuracy compared to YOLOv5s and also meets the real-time detection requirements, in which the precision, recall, and mAP are increased by 2.5, 9.0, and 8.1 percentage points, respectively, with the inference time increases by 2.7 ms to 21.8 ms. The research results provide a reference for further improving the performance of the detection of tunnel lining detection and the direction of optimization of the model reference.