RT info:eu-repo/semantics/dataPaper
T1 PY-CrackDB : a pavement crack dataset from paraguayan roads for context-aware computer vision models
A1 Ramírez Villanueva, Fredy Gabriel
A1 Vázquez Noguera, José Luis
A1 Legal Ayala, Horacio Andrés
A1 Mello Román, Julio César
A1 Pérez Estigarribia, Pastor Enmanuel
A2 Universidad Nacional de Asunción. Facultad Politécnica
AB 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.
PB Elsevier
YR 2025
FD 2025-09-12
LK http://hdl.handle.net/20.500.14066/4689
UL http://hdl.handle.net/20.500.14066/4689
LA eng
NO Ramírez-Villanueva, F. G., Vázquez Noguera, J. L., Legal-Ayala, H., Mello-Román, J. C., & Pérez-Estigarribia, P. E. (2025). PY-CrackDB: a pavement crack dataset from paraguayan roads for context-aware computer vision models. Data in Brief, 63, Article 112060.https://doi.org/10.1016/j.dib.2025.112060
NO Corresponding author. E-mail addresses: framirez@fctunca.edu.py (F.G. Ramírez-Villanueva).
NO Data accessibility:Repository name: Zenodo.Data identification number: 10.5281/zenodo.16749554.Direct URL to data: https://doi.org/10.5281/zenodo.16749554.
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 03-sep-2026