RiskIPN: Pavement Risk Database for Segmentation with Deep Learning

Uriel Escalona, Erik Zamora, Humberto Sossa

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

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.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computational Intelligence - 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, Proceedings
EditoresIldar Batyrshin, Alexander Gelbukh, Grigori Sidorov
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas69-80
Número de páginas12
ISBN (versión impresa)9783030898168
DOI
EstadoPublicada - 2021
Evento20th Mexican International Conference on Artificial Intelligence, MICAI 2021 - Mexico City, México
Duración: 25 oct 202130 oct 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13067 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia20th Mexican International Conference on Artificial Intelligence, MICAI 2021
País/TerritorioMéxico
CiudadMexico City
Período25/10/2130/10/21

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