Metricéa Lab 380: European field image dataset for concrete crack classification

This dataset, collected by Metricéa Lab, comprises 380 field images of concrete civil infrastructure, specifically curated to address the "domain shift" phenomenon in deep learning-based Structural Health Monitoring (SHM). While many existing concrete crack datasets are generated in controlled laboratory environments or scraped from the internet, these images were captured on-site during operational bridge and infrastructure inspections in the Île-de-France region. The image acquisition aligns with the rigorous criteria of the French IQOA (Image de la Qualité des Ouvrages d'Art) structural evaluation framework. The dataset is designed for binary classification tasks (Cracked vs. Uncracked) and captures the complex photometric and topological artifacts routinely encountered in real-world civil engineering. This includes variable natural lighting, shadows, surface weathering, and complex concrete textures that cause standard laboratory-trained AI models to fail in the field. This dataset has been empirically proven effective for domain adaptation: it was successfully utilized to fine-tune a pre-trained ResNet50 architecture, demonstrating that a highly constrained, field-specific dataset of only 380 images is sufficient to restore diagnostic accuracy from a degraded 14.3% (when using a laboratory baseline) to 100% on operational field data.本数据集由Metricéa实验室采集，包含380张混凝土土木基础设施的现场图像，专为解决基于深度学习的结构健康监测（Structural Health Monitoring, SHM）中的域偏移（domain shift）现象而精心构建。 当前主流的混凝土裂纹数据集多生成于受控实验室环境或从互联网爬取，而本数据集的图像均采集自法兰西岛大区的运营桥梁与基础设施实地巡检现场。图像采集严格遵循法国IQOA（Image de la Qualité des Ouvrages d'Art）结构评估框架的严苛标准。 本数据集面向二分类任务（有裂纹vs无裂纹），涵盖了真实土木工程场景中常见的复杂光度学与拓扑学伪影，包括多变的自然光照、阴影、表面风化以及复杂的混凝土纹理——这些因素会导致在实验室环境下训练的标准AI模型在实地场景中失效。 本数据集已被实证证明可有效用于域自适应（domain adaptation）任务：研究人员已成功利用该数据集对预训练ResNet50架构进行微调，证明仅含380张图像的高约束性专属现场数据集，足以将实验室基线模型的退化诊断准确率（仅14.3%）提升至实地巡检数据下的100%。