Autonomous Road Pavement Inspection and Defect Analysis for Smart City Maintenance

The detection and repair of the cracks in the road pavement is a very time consuming task which should be performed periodically in order to maintain the safety and quality of the road network. There are various types of road pavement cracks and each type requires different management and repair method and also each type indicates a different problem in that section of the road. In this paper, an autonomous machine learning based visual inspection system for detection and classification of the road pavement cracks is proposed. The proposed framework uses deep neural networks in order to detect and classify longitudinal, alligator and asphalt cracks. A dataset of images from different road conditions and various pavement cracks is collected. The proposed framework increases the speed and scale of road pavement analysis and repair and can be used for smart road maintenance management in the smart cities. The experimental results show that the accuracy of the proposed framework is 95% for detection and classification of the cracks in the road pavements.