Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection

• URL: http://arxiv.org/abs/2510.10378v1
• Date: Sun, 12 Oct 2025 00:25:33 GMT
• Title: Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection
• Authors: Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh, Andrews Danyo, Armstrong Aboah,
• Abstract summary: This paper examines the feasibility of achieving effective pixel-level crack segmentation entirely without manual annotations.<n>A fully self-supervised framework, Crack-Segmenter, is developed, integrating three complementary modules.<n>Crack-Segmenter consistently outperforms 13 state-of-the-art supervised methods across all major metrics.
• Score: 6.36678579841487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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