Tunnels are essential elements of transportation infrastructure, but are increasingly affected by ageing and deterioration mechanisms such as cracking. Regular inspections are required to ensure their safety, yet traditional manual procedures are time-consuming, subjective, and costly. Recent advances in mobile mapping systems and Deep Learning (DL) enable automated visual inspections. However, their effectiveness is limited by the scarcity of tunnel datasets. This paper introduces a new publicly available dataset containing annotated images of three different tunnel linings, capturing typical defects: cracks, leaching, and water infiltration. The dataset is designed to support supervised, semi-supervised, and unsupervised DL methods for defect detection and segmentation. Its diversity in texture and construction techniques also enables investigation of model generalization and transferability across tunnel types. By addressing the critical lack of domain-specific data, this dataset contributes to advancing automated tunnel inspection and promoting safer, more efficient infrastructure maintenance strategies.

翻译：隧道是交通基础设施的关键组成部分，但日益受到老化及开裂等劣化机制的影响。为确保其安全性需进行定期检测，然而传统人工检测方法耗时、主观性强且成本高昂。移动测量系统与深度学习的最新进展为自动化视觉检测提供了可能，但其有效性受限于隧道数据集的稀缺性。本文提出一个新型公开数据集，包含三种不同隧道衬砌的标注图像，涵盖典型缺陷类型：裂缝、渗析及渗水。该数据集专为支持有监督、半监督和无监督的深度学习缺陷检测与分割方法而设计。其在纹理与施工技术方面的多样性，还可用于研究模型在不同隧道类型间的泛化能力与可迁移性。通过弥补领域专用数据的严重不足，本数据集将推动隧道自动化检测技术的发展，促进更安全、更高效的基础设施维护策略。