Supervised classification of pathologies in asphalt pavements surface from a Remote Piloted Aircraft
Abstract
Defect identification is a routine activity in Pavement Management Systems (SGP) for decision-making about Maintenance and Rehabilitation (M&R) services. Traditional methods can be time consuming, disrupt traffic and cause accidents. In this study, pathologies on asphalt pavements were evaluated using three methods: by walking, manual classification of images from a Remotely Piloted Aircraft (RPA) and supervised classification. Manual classification resulted in 93.1% accuracy, against 32.7% in supervised classification. It is concluded that the RPA is adequate to evaluate pathologies in asphalt pavements, providing time savings and safety.
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