@inproceedings{2ed2ddc38cd6429da9942ecb8ac0d93c,
title = "RiskIPN: Pavement Risk Database for Segmentation with Deep Learning",
abstract = "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.",
author = "Uriel Escalona and Erik Zamora and Humberto Sossa",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 20th Mexican International Conference on Artificial Intelligence, MICAI 2021 ; Conference date: 25-10-2021 Through 30-10-2021",
year = "2021",
doi = "10.1007/978-3-030-89817-5_5",
language = "Ingl{\'e}s",
isbn = "9783030898168",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "69--80",
editor = "Ildar Batyrshin and Alexander Gelbukh and Grigori Sidorov",
booktitle = "Advances in Computational Intelligence - 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, Proceedings",
address = "Alemania",
}