This article proposes a new method named PICture Unsupervised Classification with Human Analysis (PICUCHA) to circumvent many of the limitations of existing approaches, based on a combination of human and artificial intelligence.
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.
This article proposes a new method named PICture Unsupervised Classification with Human Analysis (PICUCHA) to circumvent many of the limitations of existing approaches, based on a combination of human and artificial intelligence.
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12 p.
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Abstract: The pavement surface condition assessment is a critical component for a proper pavement management system as well as for pavement rehabilitation design. A number of devices were developed to automatically record surface distresses in a continuous survey mode, but the software required for automatic distress identification remains a big challenge. In this study, a new method named PICture Unsupervised Classification with Human Analysis (PICUCHA) is proposed to circumvent many of the limitations of existing approaches, based on a combination of human and artificial intelligence. It was designed from scratch to be capable to identify sealed and unsealed cracks, potholes, patches, different types of pavements and others. The self-learning algorithms do not use any distresses predefinition and can process images taken by cameras with different brands, technologies and resolution. This study describes some key aspects of the new method and provides examples in which PICUCHA was tested in real conditions showing accuracy up to 96.9% in image pattern detection and classification.
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Salini, Reus; Xu, Bugao & Carvalho, Regis.Hybrid Human-Artificial Intelligence Approach for Pavement Distress Assessment (PICUCHA),
article,
July 2017;
Prague, Czech Republic.
(https://digital.library.unt.edu/ark:/67531/metadc990977/:
accessed May 30, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Merchandising, Hospitality and Tourism.