构建首个全球统一的路面病害数据集，助力检测模型跨场景泛化。
PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification
- 整合7国数据，统一标注格式与病害分类标准。
- 含52747张图像、13.5万+边界框，覆盖13类病害。
- 支持模型公平对比，可零样本迁移至新环境。
自动路面缺陷检测因缺乏标准化数据集而难以在多样真实条件下泛化。现有数据集在标注风格、病害定义和格式上差异显著，限制了联合训练。为此，我们提出一个综合性基准数据集，将多个公开来源整合为统一数据集，包含来自7个国家的52,747张图像，共135,277个边界框标注，覆盖13种不同病害类型。数据集涵盖广泛的图像质量、分辨率、视角和天气变化，为一致训练与评估提供独特资源。通过在YOLOv8-YOLOv12、Faster R-CNN和DETR等先进目标检测模型上进行基准测试，验证了其有效性，模型在多种场景下均表现良好。通过统一类别定义与标注格式，该数据集成为首个具有全球代表性的路面病害检测基准，支持模型公平比较，并实现对新环境的零样本迁移。
原文摘要 · Abstract (English)
Automated pavement defect detection often struggles to generalize across diverse real-world conditions due to the lack of standardized datasets. Existing datasets differ in annotation styles, distress type definitions, and formats, limiting their integration for unified training. To address this gap, we introduce a comprehensive benchmark dataset that consolidates multiple publicly available sources into a standardized collection of 52747 images from seven countries, with 135277 bounding box annotations covering 13 distinct distress types. The dataset captures broad real-world variation in image quality, resolution, viewing angles, and weather conditions, offering a unique resource for consistent training and evaluation. Its effectiveness was demonstrated through benchmarking with state-of-the-art object detection models including YOLOv8-YOLOv12, Faster R-CNN, and DETR, which achieved competitive performance across diverse scenarios. By standardizing class definitions and annotation formats, this dataset provides the first globally representative benchmark for pavement defect detection and enables fair comparison of models, including zero-shot transfer to new environments.
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