Advancement in Pavement Condition Assessment: an AI-Based Computer Vision Approach

Integrating artificial intelligence and advanced machine learning from manual inspection methodologies to advanced AI-driven techniques for enhancing pavement condition assessment is an indispensable need today. Conventional monitoring and assessment methods do not meet increased infrastructure maintenance and vehicular traffic requirements. The research validates the significant improvement in accuracy and efficiency by integrating traditional methods with emerging computer vision-based technologies. Remarkably, the proposed model achieved a remarkable accuracy of 98.98% and a loss rate of 0.1853, showcasing superior performance compared to established models such as VGG19 and ResNet50, which recorded 95.91% and 96.18%, respectively. These findings highlight the efficiency of AI in real-time, large-scale deployment for road maintenance and safety augmentations. Although there are encouraging outcomes, obstacles remain related to reliance on data and computing requirements in situations with limited resources. Future research will focus on enhancing data efficiency, exploring adaptive AI techniques, and expanding model validation across diverse geographical settings to optimize global applicability. The successful implementation of these technologies promises to revolutionize pavement maintenance practices and pave the way for proactive strategies that enhance road safety and operational efficiency, marking a substantial advancement in civil engineering infrastructure management.