Engineering Commons^™

Deep Learning Techniques For Efficient Evaluation Of Asphalt Pavement Condition, Kamel Mahdy, Ahmed Zekry, Mohamed Moussa, Ahmed Mohamed, Hassan Mahdy, Mohamed Elhabiby

Mansoura Engineering Journal

For the last few decades, researchers have been devising a simple and cost-effective method to evaluate pavement distresses to give decision-makers adequate feedbacks about the pavement condition of a certain road. Fortunately, with the evolution and progression of computer vision tools and techniques, good results had been achieved regarding the detection, classification, and quantification of road distress. In this paper, a new efficient process of road distress analysis using deep learning models is introduced. This new process was tested on a collected road dataset to evaluate the efficiency and speed of this low-cost road maintenance system. Promising results were obtained …