Enabling Real-Time, Cost-Efficient, and Lightweight High-Speed Crack Segmentation using Self-Supervised Attention Mechanism
Abstract This study proposes a lightweight and high-performance crack segmentation framework based on a MobileNet U-Net architecture, enhanced through self-supervised learning (SSL) and a multi-head attention mechanism. The encoder is pretrained using a self-supervised image inpainting task on grays...
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| Автори: | , , , , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Springer
2026-04-01
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| Серія: | International Journal of Computational Intelligence Systems |
| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1007/s44196-026-01306-y |
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