Intelligent Pavement Recognition and Automated Repair Algorithm Based on LU-Net and DP

Abstract

Crack repair has long been a critical task in highway maintenance. With the continuous expansion of the scale of roads under maintenance, traditional manual patching can no longer meet the growing demand. Current mainstream pavement crack repair technologies generally have the shortcoming that real-time performance and accuracy are difficult to balance. To address the dual problems of insufficient crack detection accuracy and low efficiency in automated repair path planning, this study is committed to developing an efficient intelligent repair method. It proposes a two-stage algorithm integrating intelligent crack detection and automated repair. In the intelligent detection stage, a lightweight U-shaped hybrid network (LU-Net), which combines U-Net and MobileNetV2, is designed to facilitate intelligent crack identification and trajectory extraction. In the path planning stage, an exact algorithm based on dynamic programming (DP) is introduced to determine the optimal repair sequence automatically. Comparative experiments verified the effectiveness and accuracy of the method: the detection accuracy of LU-Net reached 88.35% with a mean intersection over union (MIoU) of 80.39%; for scenarios with 5–14 crack paths, the proposed dynamic programming algorithm not only found the global optimal solution but also reduced the solution time by 90% compared with existing exact algorithms. The results show that the LU-Net segmentation model and DP algorithm can significantly save operation time while ensuring accuracy.