Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Datos identificativos

    Identificador: imarina:9380775
    Autores:
    Okran, Ammar MAbdel-Nasser, MohamedRashwan, Hatem APuig, Domenec
    Resumen:
    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.
  • Otros:

    Autor según el artículo: Okran, Ammar M; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Puig, Domenec
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Palabras clave: Deep learning Instance segmentatio Instance segmentation Mask-rcnn Object detection Road crack
    Resumen: 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.
    Áreas temáticas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: domenec.puig@urv.cat hatem.abdellatif@urv.cat mohamed.abdelnasser@urv.cat
    Identificador del autor: 0000-0002-0562-4205 0000-0001-5421-1637 0000-0002-1074-2441
    Fecha de alta del registro: 2024-09-21
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://ebooks.iospress.nl/doi/10.3233/FAIA220342
    Referencia al articulo segun fuente origial: Frontiers In Artificial Intelligence And Applications. 356 225-228
    Referencia 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 Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3233/FAIA220342
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    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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