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A Curated Dataset for Crack Image Analysis: Experimental Verification and Future Perspectives

  • Dades identificatives

    Identificador: imarina:9380775
    Autors:
    Okran, Ammar MAbdel-Nasser, MohamedRashwan, Hatem APuig, Domenec
    Resum:
    Most crack image datasets are developed for crack segmentation or detection. They cannot be used to train a deep learning model to detect and segment cracks simultaneously. Most of existing datasets do not include a very accurate annotation. Besides, some crack images cannot be used to train deep learning models because of their inferior quality. In this paper, we propose a promising curated crack image dataset that allows the development of crack segmentation, detection, and classification on the same set of images simultaneously. There is no dataset for road crack that involves detection and segmentation tasks to the best of our knowledge. The current version of the curated database consists of 506 images derived from the RDD2020 dataset taken from multi-countries (Japan, Czech, and India). We use the curated dataset to build different deep learning-based crack detection and segmentation methods. Our experiments demonstrate that the proposed dataset yields promising results for crack detection and segmentation.
  • Altres:

    Autor segons l'article: Okran, Ammar M; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Puig, Domenec
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Paraules clau: Deep learning Instance segmentatio Instance segmentation Mask-rcnn Object detection Road crack
    Resum: Most crack image datasets are developed for crack segmentation or detection. They cannot be used to train a deep learning model to detect and segment cracks simultaneously. Most of existing datasets do not include a very accurate annotation. Besides, some crack images cannot be used to train deep learning models because of their inferior quality. In this paper, we propose a promising curated crack image dataset that allows the development of crack segmentation, detection, and classification on the same set of images simultaneously. There is no dataset for road crack that involves detection and segmentation tasks to the best of our knowledge. The current version of the curated database consists of 506 images derived from the RDD2020 dataset taken from multi-countries (Japan, Czech, and India). We use the curated dataset to build different deep learning-based crack detection and segmentation methods. Our experiments demonstrate that the proposed dataset yields promising results for crack detection and segmentation.
    Àrees temàtiques: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: domenec.puig@urv.cat hatem.abdellatif@urv.cat mohamed.abdelnasser@urv.cat
    Identificador de l'autor: 0000-0002-0562-4205 0000-0001-5421-1637 0000-0002-1074-2441
    Data d'alta del registre: 2024-09-21
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://ebooks.iospress.nl/doi/10.3233/FAIA220342
    Referència a l'article segons font original: Frontiers In Artificial Intelligence And Applications. 356 225-228
    Referència de l'ítem segons les normes APA: Okran, Ammar M; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Puig, Domenec (2022). A Curated Dataset for Crack Image Analysis: Experimental Verification and Future Perspectives. Amsterdam: IOS Press
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.3233/FAIA220342
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Proceedings Paper
  • Paraules clau:

    Artificial Intelligence
    Deep learning
    Instance segmentatio
    Instance segmentation
    Mask-rcnn
    Object detection
    Road crack
    Artificial intelligence
    Ciências agrárias i
    Comunicació i informació
    Engenharias iii
    Engenharias iv
    General o multidisciplinar
    Información y documentación
    Interdisciplinar
    Medicina ii
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