Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908606633345024 |
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| author | Kyem, Blessing Agyei Asamoah, Joshua Kofi Denteh, Eugene Danyo, Andrews Aboah, Armstrong |
| author_facet | Kyem, Blessing Agyei Asamoah, Joshua Kofi Denteh, Eugene Danyo, Andrews Aboah, Armstrong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection Kyem, Blessing Agyei Asamoah, Joshua Kofi Denteh, Eugene Danyo, Andrews Aboah, Armstrong Computer Vision and Pattern Recognition 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. |
| title | Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.10378 |