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Abstract The most popular form of road surface is asphalt pavement. The serviceability of this structure has a direct impact on today’s society and is significantly linked to regional economic growth. As a result, one of the most important responsibilities of the transportation authorities is to guarantee the condition of the asphalt road surface during its serviceability period by proactively detecting road distresses and undertaking timely repair. This time factor is also important as early works have shown that inadequate maintenance may dramatically affect costs over the pavement’s lifetime. For the last few decades, scientists and researchers have been trying to find a simple and cost-effective method to apprise decision-makers of the pavement condition of the road network in a timely manner as a part of the Pavement Maintenance Management System (PMMS). Fortunately, and with the evolution of computer vision tools and techniques, very good results have been achieved regarding auto-detection, classification, and quantification of road distress. Conversely, the traditional pavement assessment methods, without a doubt, have got many disadvantages in terms of time, money, safety, and expertise. In this research, two computer vision models were introduced using a new mainstream deep learning framework, Google NASNet and Facebook AI (YOLO) model. This new family of models is the product of a deep learning prediction model, which is a revolutionary approach that has been used worldwide over the past few years. TensorFlow deeplearning framework is used to run the model. Promising results are obtained from the proposed model with a performance of " ~ "400 FPS and distress detection every " ~ "5 cm for a 40-km/h moving vehicle. Furthermore, the output of the developed model will be used as an input for our pavement rating calculation module to determine the pavement condition. The proposed system focuses on specific types of distresses where most of them show a substantial effect on the overall pavement condition: Alligator cracks, Longitudinal cracks, Transverse cracks, Block cracks, Potholes, and Bleeding. Experimental results show that the model based on Google NASNet achieves promising prediction performance. Commercial software is used to extract a manually labeled and annotated data set of approximately 2,700 images extracted from videos captured using a smartphone camera mounted on the bumper of a personal automobile for some roads in Cairo, Egypt, which is then processed using deep-learning software specifically designed for this research. In conclusion, we can say that our proposed solution has a potential to lead to a breakthrough in the economics of road inspection methods because of many reasons; first of all, it is real-time, cost-effective method for gathering and analyzing data within PMMS replacing high-end alternatives, moreover, classification model showed a 90% reduction in model size and approximately 400% increase in inference speed compared to the current state – of – the – art models with an outstanding precision of detection more than 97%. Needless to say, that results showed that the proposed model can work perfectly in Egypt since it was trained on pavement distress from Egypt. |