无需人工标注，用自监督模型实现高效路面裂缝检测
Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection
- 构建三模块自监督框架，融合多尺度特征与方向注意力
- 在10个数据集上超越13种主流有监督方法，mIoU等指标领先
- 适合交通部门做大规模基础设施低成本巡检
路面裂缝检测长期依赖昂贵且耗时的像素级标注，限制了其在大规模基础设施监测中的可扩展性。为突破这一瓶颈，本文探索完全无需人工标注即可实现有效像素级裂缝分割的可行性。基于此目标，提出全自监督框架Crack-Segmenter，包含三个互补模块：用于鲁棒多尺度特征提取的尺度自适应嵌入器（SAE）、保持线状裂缝连续性的方向注意力变换器（DAT），以及用于自适应特征融合的注意力引导融合（AGF）模块。在10个公开数据集上的评估显示，Crack-Segmenter在所有主要指标（包括平均交并比mIoU、Dice分数、XOR和豪斯多夫距离HD）上均持续优于13种先进有监督方法。结果表明，无标注裂缝检测不仅可行，且表现更优，使交通部门和基础设施管理者能够开展可扩展、低成本的监测工作。该研究推动了自监督学习发展，激励了路面裂缝检测研究。
原文摘要 · Abstract (English)
Pavement crack detection has long depended on costly and time-intensive pixel-level annotations, which limit its scalability for large-scale infrastructure monitoring. To overcome this barrier, this paper examines the feasibility of achieving effective pixel-level crack segmentation entirely without manual annotations. Building on this objective, a fully self-supervised framework, Crack-Segmenter, is developed, integrating three complementary modules: the Scale-Adaptive Embedder (SAE) for robust multi-scale feature extraction, the Directional Attention Transformer (DAT) for maintaining linear crack continuity, and the Attention-Guided Fusion (AGF) module for adaptive feature integration. Through evaluations on ten public datasets, Crack-Segmenter consistently outperforms 13 state-of-the-art supervised methods across all major metrics, including mean Intersection over Union (mIoU), Dice score, XOR, and Hausdorff Distance (HD). These findings demonstrate that annotation-free crack detection is not only feasible but also superior, enabling transportation agencies and infrastructure managers to conduct scalable and cost-effective monitoring. This work advances self-supervised learning and motivates pavement cracks detection research.
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