TACK Tunnel Data (TTD): A Benchmark Dataset for Deep Learning-Based Defect Detection in Tunnels

TL;DR

This paper introduces TACK Tunnel Data (TTD), a comprehensive dataset of annotated tunnel images to facilitate deep learning-based defect detection, aiming to improve automated inspection accuracy and efficiency.

Contribution

The paper presents a new publicly available dataset with diverse tunnel defect images, supporting various deep learning methods and enabling research on model generalization across tunnel types.

Findings

Dataset includes images of cracks, leaching, and water infiltration.

Supports supervised, semi-supervised, and unsupervised learning methods.

Enhances research on automated tunnel defect detection.

Abstract

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.