Impact of Image Resolution Pavement Distress Detection Using PICUCHA Methodology

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This article describes a study focused on the analysis of a newly-developed pavement distress classification algorithm, called the PICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly the impact of image resolutions on its classification accuracy.

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11 p.

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Salini, Reus; Xu, Bugao & Souliman, Mena December 2016.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Merchandising, Hospitality and Tourism to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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UNT College of Merchandising, Hospitality and Tourism

The UNT College of Merchandising, Hospitality, and Tourism educates students for the globalization of the hospitality, retail, and tourism industries. The College provides bachelor's and master's programs in a variety of majors.

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Description

This article describes a study focused on the analysis of a newly-developed pavement distress classification algorithm, called the PICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly the impact of image resolutions on its classification accuracy.

Physical Description

11 p.

Notes

Abstract: An accurate and regular survey of the road surface distresses is a key factor for pavement rehabilitation design and management, allowing public managers to maximize the value of the continuously limited budgets for road improvements and maintenance. Manual pavement distress surveys are labor-intensive, expensive and unsafe for highly-trafficked highways. Over the years, automated surveys using various hardware devices have been developed and improved for pavement field data collection to solve the problems associated with manual surveys. However, the reliable distress detection software and the data analysis remain challenging. This study focused on the analysis of a newly-developed pavement distress classification algorithm, called the PICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly the impact of image resolutions on its classification accuracy. The results show that a non-linear relationship exists between the classification accuracy and the image resolution, suggesting that images with a resolution around 1.24 mm/pixel may provide the optimal classification accuracy when using the PICUCHA method. The findings of this study can help to improve more effective uses of the specialize software for pavement distress classification, to support decision makers to choose cameras according to their budgets and desired survey accuracy, and to evaluate how existing cameras will perform if used with PICUCHA.

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  • Stavebni Obzor - Civil Engineering Journal, 2016. Prague, Czech Republic: Czech Technical University.

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Publication Information

  • Publication Title: Stavebni Obzor - Civil Engineering Journal
  • Pages: 1-11
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • December 2016

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  • Aug. 29, 2017, 9:38 a.m.

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Salini, Reus; Xu, Bugao & Souliman, Mena. Impact of Image Resolution Pavement Distress Detection Using PICUCHA Methodology, article, December 2016; Prague, Czech Republic. (digital.library.unt.edu/ark:/67531/metadc991032/: accessed April 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Merchandising, Hospitality and Tourism.