Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study investigates whether leveraging multi-temporal data for crack segmentation can enhance segmentation quality. Therefore, we compare a Swin UNETR trained on multi-temporal data with a U-Net trained on mono-temporal data to assess the effect of temporal information compared with conventional single-epoch approaches. To this end, a multi-temporal dataset comprising 1356 images, each with 32 sequential crack propagation images, was created. After training the models, experiments were conducted to analyze their generalization ability, temporal consistency, and segmentation quality. The multi-temporal approach consistently outperformed its mono-temporal counterpart, achieving an IoU of $82.72\%$ and a F1-score of $90.54\%$, representing a significant improvement over the mono-temporal model's IoU of $76.69\%$ and F1-score of $86.18\%$, despite requiring only half of the trainable parameters. The multi-temporal model also displayed a more consistent segmentation quality, with reduced noise and fewer errors. These results suggest that temporal information significantly enhances the performance of segmentation models, offering a promising solution for improved crack detection and the long-term monitoring of concrete structures, even with limited sequential data.

本研究着重于解决混凝土结构裂缝检测中的早期自动化问题，比较了多时间数据和单时间数据在裂缝分割中的效果。通过使用多时间数据训练的Swin UNETR模型显著提高了分割质量，其IoU达到82.72%，F1-score达到90.54%，显示出相较于单时间模型的显著改进，这为混凝土结构的长期监测提供了有希望的解决方案。

基于深度学习方法的混凝土结构多时间裂缝分割