PY-CrackDB: A pavement crack dataset from paraguayan roads for context-aware computer vision models

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-12 DOI:10.1016/j.dib.2025.112060

Fredy Gabriel Ramírez-Villanueva , José Luis Vázquez Noguera , Horacio Legal-Ayala , Julio César Mello-Román , Pastor Enmanuel Pérez-Estigarribia

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引用次数: 0

Abstract

PY-CrackDB, a novel dataset of asphalt pavement images designed for developing context-aware artificial intelligence systems. The dataset contains 569 images (351 × 500 pixels), collected from national routes near Coronel Oviedo, Paraguay, and divided into 369 images with cracks and 200 without. A primary contribution of this work is its specific focus on fine fissures (< 3 mm wide), a category critical for early-stage maintenance according to Paraguayan road engineering standards. Data collection was performed under standardized conditions, and all annotations were created by civil engineering professionals and subsequently verified through a rigorous cross-review protocol to ensure accuracy. This methodological rigor resulted in a dataset that is particularly suitable for training and validating models for semantic segmentation and early defect detection, ultimately supporting the development of preventative road maintenance strategies.

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PY-CrackDB：巴拉圭道路的路面裂缝数据集，用于上下文感知计算机视觉模型

PY-CrackDB，一个新的沥青路面图像数据集，专为开发上下文感知人工智能系统而设计。该数据集包含569幅图像（351 × 500像素），采集自巴拉圭Coronel Oviedo附近的国家路线，分为369幅有裂缝的图像和200幅没有裂缝的图像。这项工作的一个主要贡献是它特别关注细裂缝（3毫米宽），根据巴拉圭道路工程标准，这是早期维护的关键类别。数据收集是在标准化条件下进行的，所有注释都是由土木工程专业人员创建的，随后通过严格的交叉审查协议进行验证，以确保准确性。这种方法的严谨性产生了一个特别适合训练和验证语义分割和早期缺陷检测模型的数据集，最终支持预防性道路维护策略的开发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.