Development and Application of an AI-Based Automatic Identification System for Rural Road Distress and Maintenance Management

With the continuous expansion of rural road construction and increasing management demands, traditional rural road inspection and maintenance models are becoming insufficient to meet current needs. The analysis of inspection results and the development of maintenance plans are often delayed. To address these challenges, this paper proposes a rural road distress sample recognition and annotation method based on machine vision techniques, and establishes a corresponding disease target identification sample database. The method is trained and validated using the U-Net algorithm, achieving an accuracy of 94.95%. Additionally, a lightweight detection system is developed to facilitate rural road surface disease target detection and automatic recognition. The self-developed automatic recognition system significantly enhances the accuracy and efficiency of pavement disease recognition. Furthermore, a management platform has been implemented to enable the dynamic management of rural road disease data and maintenance operations.