摘要

路面裂缝是道路基础设施劣化的主要形式，需高效精准检测以实现及时养护。现有检测方法要么依赖人工巡检，劳动密集；要么受限于高硬件成本与GPS依赖，自动化系统灵活性不足，难以实现连续路面评估。本文提出一种双通道裂缝检测模型，融合连续路面纹理重建、深度分割与高精度边界精修算法，实现纵向裂缝的现场检测与精度提升。研究开发了基于特征的图像拼接算法，通过高分辨率图像重建连续路面纹理，实现无需GPS的裂缝定位。该方法进一步结合YOLOv8-seg模型与自适应形态学操作，实现像素级裂缝重建。对比实验表明，混合方法较基线模型具有更优的分割性能、更精细的边界刻画及更完整的分支恢复能力。本文为纵向裂缝的自动化检测与高保真重建提供了实用方案，有效支撑路面养护决策。

Pavement cracks are a major form of road infrastructure degradation, necessitating efficient and accurate detection for timely maintenance. Existing inspection methods rely either on labor-intensive manual surveys or automated systems constrained by high hardware costs and GPS dependency, limiting their flexibility for continuous surface assessment. This paper introduces a dual-channel crack detection model that integrates continuous pavement texture reconstruction with deep segmentation and high-precision boundary refinement algorithms, enabling on-site implementation and accuracy enhancement for longitudinal crack detection. A feature-based image stitching algorithm is developed to reconstruct continuous pavement textures from high-resolution images, enabling GPS-free crack localization. The proposed method further combines the YOLOv8-seg model with adaptive morphological operations to achieve pixel-level crack reconstruction. Comparative experiments reveal that the hybrid approach achieves superior segmentation performance with finer boundary delineation and improved branch recovery compared to the baseline model. The paper provides a practical solution for automated inspection and high-fidelity reconstruction of longitudinal cracks, effectively supporting pavement maintenance planning.

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elopment Program of China (grant number 2022YFC3801100); and Shenzhen Science and Technology Program (grant number SGDX 20240115110503006).

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