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RiskIPN: Pavement Risk Database for Segmentation with Deep Learning

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Advances in Computational Intelligence (MICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13067))

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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

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Acknowledgments

Authors would like to acknowledge the support provided by the Instituto Politecnico Nacional under projects: SIP 20210788, SIP 20210316 and CONACYT under projects: 065 (Fronteras de la Ciencia) and 6005 (FORDECYT PRONACES) to carry out this research. Uriel Escalona thanks CONACYT for the scholarship granted towards pursuing his PhD studies. Authors acknowledge the support of social service students of the Instituto Politecnico Nacional for the creation of the database.

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Authors and Affiliations

  1. Instituto Politécnico Nacional, CIC, Av. Juan de Dios Batiz S/N, Col. Nueva Industrial Vallejo, Gustavo A. Madero, 07738, Ciudad de México, Mexico

    Uriel Escalona, Erik Zamora & Humberto Sossa

  2. Tecnológico de Monterrey, Campus Guadalajara. Av. Gral. Ramón Corona 2514, 45138, Zapopan, Jalisco, Mexico

    Humberto Sossa

Authors
  1. Uriel Escalona
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  2. Erik Zamora
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  3. Humberto Sossa
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Corresponding author

Correspondence to Uriel Escalona .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Ildar Batyrshin

  2. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Alexander Gelbukh

  3. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

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Escalona, U., Zamora, E., Sossa, H. (2021). RiskIPN: Pavement Risk Database for Segmentation with Deep Learning. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Computational Intelligence. MICAI 2021. Lecture Notes in Computer Science(), vol 13067. Springer, Cham. https://doi.org/10.1007/978-3-030-89817-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-89817-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89816-8

  • Online ISBN: 978-3-030-89817-5

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