Multi-temporal crack segmentation in concrete structures using deep learning approaches

TL;DR

This paper demonstrates that using multi-temporal data with deep learning significantly improves crack segmentation accuracy and consistency in concrete structures compared to single-epoch methods.

Contribution

It introduces a multi-temporal dataset and compares a Swin UNETR with a U-Net, showing the benefits of temporal information for crack segmentation.

Findings

Multi-temporal approach achieved 82.72% IoU and 90.54% F1-score.

Multi-temporal model required half the parameters of mono-temporal model.

Temporal data improves segmentation quality and reduces errors.

Abstract

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.