A review of advanced pavement distress evaluation techniques using unmanned aerial vehicles

ABSTRACT

The economy of the nation is significantly influenced by pavement management systems. The use of cutting-edge techniques and technology in various pavement management system applications, as well as research into these topics, are particularly important today. To reduce human errors in data collection, advances in technology and automated surveillance should be used. The most recent methods used in modern equipment for the automatic evaluation of pavement deterioration are reviewed in this paper. To identify trending tools, research gaps, emerging technologies, challenges and limitations of using computer vision for pavement distress and condition assessment, papers collected using UAV (Unmanned Ariel Vehicle) data collection and the application of machine learning methods are investigated. The review comes to the conclusion that the application of machine learning techniques is the general trend in evaluating the condition of pavements, despite some limitations not only in the detection of a small number of pavement distresses with complex patterns but also in the indication of the severity and density of distresses, opening up possibilities for further study.

KEYWORDS:

• UAV (unmanned ariel vehicle)
• machine learning & deep learning techniques
• automatic pavement distress detection
• distress severity
• 3D surface modelling

Disclosure statement

No potential conflict of interest was reported by the author(s).