StructDamage: 一个大规模统一裂缝和表面缺陷数据集用于鲁棒结构损伤检测
StructDamage:A Large Scale Unified Crack and Surface Defect Dataset for Robust Structural Damage Detection
AI总结：
StructDamage数据集通过大规模统一裂缝和表面缺陷图像，为鲁棒结构损伤检测提供全面资源，提升模型泛化能力与检测精度。
摘要：
自动化检测和分类结构裂缝和表面缺陷是土木工程、基础设施维护和遗产保护中的关键挑战。近年来，计算机视觉（CV）和深度学习（DL）的进展显著提高了自动裂缝检测的水平。然而，这些方法严重依赖于大型、多样且精心编纂的数据集，这些数据集包含不同表面材料上的各种裂缝类型。许多现有的公共裂缝数据集缺乏地理多样性、表面类型、尺度和标注一致性，使训练算法在现实条件下难以有效泛化。我们提供了一个新的数据集，StructDamage，一个精心编纂的包含约78,093张图像的集合，涵盖九种表面类型：墙壁、瓷砖、石头、道路、路面、桥面、混凝土和砖块。该数据集通过系统地聚合、调和和重新标注32个公开可用的数据集中的图像构建而成，这些数据集涵盖混凝土结构、沥青路面、砖石墙壁、桥梁和历史建筑。所有图像都按文件夹级别分类层次组织，适合训练卷积神经网络（CNN）和视觉变换器。为了突出该数据集的实用价值，我们使用来自六个模型家族的十五种深度学习架构进行了基线分类结果展示，其中十二种模型的宏F1分数超过0.96。表现最好的模型DenseNet201达到98.62%的准确率。所提出的数据集提供了一个全面且多功能的资源，适用于分类任务。通过详尽的文档和标准结构，它被设计用于促进可重复研究，并支持鲁棒裂缝损伤检测方法的发展和公平评估。
英文摘要：
Automated detection and classification of structural cracks and surface defects is a critical challenge in civil engineering, infrastructure maintenance, and heritage preservation. Recent advances in Computer Vision (CV) and Deep Learning (DL) have significantly improved automatic crack detection. However, these methods rely heavily on large, diverse, and carefully curated datasets that include various crack types across different surface materials. Many existing public crack datasets lack geographic diversity, surface types, scale, and labeling consistency, making it challenging for trained algorithms to generalize effectively in real world conditions. We provide a novel dataset, StructDamage, a curated collection of approximately 78,093 images spanning nine surface types: walls, tile, stone, road, pavement, deck, concrete, and brick. The dataset was constructed by systematically aggregating, harmonizing, and reannotating images from 32 publicly available datasets covering concrete structures, asphalt pavements, masonry walls, bridges, and historic buildings. All images are organized in a folder level classification hierarchy suitable for training Convolutional Neural Networks (CNNs) and Vision Transformers. To highlight the practical value of the dataset, we present baseline classification results using fifteen DL architectures from six model families, with twelve achieving macro F1-scores over 0.96. The best performing model DenseNet201 achieves 98.62% accuracy. The proposed dataset provides a comprehensive and versatile resource suitable for classification tasks. With thorough documentation and a standard structure, it is designed to promote reproducible research and support the development and fair evaluation of robust crack damage detection approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
