公开隧道缺陷数据集，助力智能巡检模型训练与泛化研究
TACK Tunnel Data (TTD): A Benchmark Dataset for Deep Learning-Based Defect Detection in Tunnels
- 构建三类衬砌的带标注图像数据集，覆盖裂缝、渗漏等典型缺陷
- 支持监督、半监督及无监督学习，推动自动化检测算法发展
- 适合从事智能巡检、工业视觉、基础设施维护的研究者使用
隧道是交通基础设施的关键组成部分，但日益受到老化和劣化的影响，如开裂。定期检查对保障其安全至关重要，但传统人工检测耗时、主观且成本高。近年来，移动测绘系统与深度学习（DL）的发展使自动化视觉检测成为可能。然而，其效果受限于隧道专用数据集的匮乏。本文提出一个公开可用的新数据集，包含三种不同隧道衬砌的带标注图像，涵盖典型缺陷：裂缝、渗漏和水浸。该数据集支持监督、半监督和无监督深度学习方法在缺陷检测与分割中的应用。其在纹理和施工技术上的多样性，也支持模型泛化与跨隧道类型迁移能力的研究。通过填补领域特定数据的空白，该数据集有助于推进自动化隧道检测，促进更安全高效的基础设施维护策略。
原文摘要 · Abstract (English)
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.
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