Design and Implementation of Regional Identification Algorithm for Highway Pavement Diseases Based on UAV Vision and CNN

With the continuous expansion of highway networks in recent years, the monitoring and repair of road diseases have become one of the important tasks in traffic management. Traditional manual inspection methods are inefficient and prone to missing disease areas. Therefore, a pavement disease detection system based on UAV vision and deep learning algorithms has emerged. The formation mechanism of pavement diseases was first analyzed, and the UAV pavement image acquisition equipment was introduced. Then, a pavement disease identification algorithm using YOLOv8 was constructed. Finally, Python programming was employed to complete the construction of a regional identification model for highway pavement diseases based on CNN, data loading, and model training and testing. The experimental results show that the system exhibits high accuracy and real-time performance in image, video, and real-time camera identification, effectively assisting in the monitoring of highway diseases. This system can significantly improve the automation level of pavement disease detection, reduce manual workload, enhance road management efficiency, and provide strong support for the safety and maintenance of highways.