Detection of Pavement Damage Using Smartphones

Early detection and repair of pavement damage can significantly reduce the associated maintenance and repair costs. In recent years, efforts have been made to use digital hardware and software to streamline the inspection and diagnostic process. However, in a real-world scenario, the resource limitations, high cost, and time-consuming nature of these digital units have diminished their efficiency. In the past decade, smartphones have gained remarkable hardware capabilities. Mobile phones, aided by GPS, record location information and capture high-quality images with powerful lenses. This research aims to utilize machine learning algorithms to employ smartphones for pavement inspection. Deep learning, a method capable of pattern recognition and solving complex problems, has been chosen as the machine learning technique. The learning process of this algorithm is conducted using samples collected from pavement surfaces via smartphones. The study further aims to enhance the speed and processing power of the learning method through new parameters. Additionally, a defined framework is provided to assess the quality of damage, facilitating effective action by route managers.

Keywords:

Machine Learning , Deep Learning , Pavement , diagnostics