| Título |
RiskIPN: Pavement Risk Database for Segmentation with Deep Learning |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
20th Mexican International Conference on Artificial Intelligence, MICAI 2021 |
| Resumen |
A large number of car accidents are caused by failures in the pavement. Their automatic detection is important for pavement maintenance, however, the current public datasets of images to train and test these systems contain a few hundred samples. In this paper, we introduce a new large dataset of images with more than 2000 samples that contains the five most common risks on pavement manually annotated. We analyze and describe statistically the properties of this dataset and we establish the performance of some baseline methods in order to be useful as a benchmark. We achieve up to 89.35% accuracy in the segmentation of the different types of risk on the pavement. © 2021, Springer Nature Switzerland AG. |
| Observaciones |
DOI 10.1007/978-3-030-89817-5_5
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Lugar |
Ciudad de México |
| País |
Mexico |
| No. de páginas |
69-80 |
| Vol. / Cap. |
13067 LNAI |
| Inicio |
2021-10-25 |
| Fin |
2021-10-30 |
| ISBN/ISSN |
9783030898168 |
