A Deep Learning based Process Model for Crack Detection in Pavement Structures

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper presents an investigation of the effectiveness of a Machine Deep Learning (DL) model using Convolution Neural Networks (CNN) in detecting cracks in asphalt pavement. Pavement cracks have been studied for decades using traditional Machine Learning (ML) methods such as neural networks, genetic algorithms, fuzzy logic, etc. In this work, we present a DL model based on CNN to study the effectiveness of modern machine vision methods on an old problem of pavement crack detection. We provide our methodology in developing the proposed model and the validation process in this paper. A dataset consisting of 500 sample images was used to test the model and our experiments showed that the proposed model is effective with a mean accuracy of 96% and a standard deviation of. 025. Future work is recommended to be on crack type classification after the successful detection process.

Original languageEnglish
Title of host publicationProceedings of the 2022 9th International Conference on Computing for Sustainable Global Development, INDIACom 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9789380544441
DOIs
StatePublished - 2022
Event9th International Conference on Computing for Sustainable Global Development, INDIACom 2022 - New Delhi, India
Duration: 23 Mar 202225 Mar 2022

Publication series

NameProceedings of the 2022 9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

Conference

Conference9th International Conference on Computing for Sustainable Global Development, INDIACom 2022
Country/TerritoryIndia
CityNew Delhi
Period23/03/2225/03/22

Keywords

  • crack detection
  • deep learning
  • image processing
  • machine vision
  • neural networks
  • Pavement cracks

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