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.

翻译：裂缝是混凝土结构劣化的最早迹象之一。对这些裂缝进行早期自动检测，能显著延长桥梁、建筑和隧道等关键基础设施的使用寿命，同时降低维护成本并促进高效的结构健康监测。本研究探讨了利用多时序数据进行裂缝分割是否能提升分割质量。为此，我们比较了在多时序数据上训练的 Swin UNETR 与在单时序数据上训练的 U-Net，以评估时序信息相较于传统单时相方法的效果。为此，我们创建了一个包含1356张图像的多时序数据集，每张图像包含32幅连续的裂缝扩展序列图像。在模型训练完成后，我们进行了实验以分析其泛化能力、时序一致性和分割质量。多时序方法始终优于单时序方法，其交并比（IoU）达到 $82.72\%$，F1分数达到 $90.54\%$，相较于单时序模型的 IoU $76.69\%$ 和 F1分数 $86.18\%$ 有显著提升，且所需可训练参数仅为后者的一半。多时序模型还表现出更一致的分割质量，噪声更少，错误更少。这些结果表明，时序信息显著提升了分割模型的性能，为改进裂缝检测和混凝土结构的长期监测提供了一个有前景的解决方案，即使在序列数据有限的情况下也是如此。