Crack segmentation is crucial in civil engineering, particularly for assessing pavement integrity and ensuring the durability of infrastructure. While deep learning has advanced RGB-based segmentation, performance degrades under adverse conditions like low illumination or motion blur. Thermal imaging offers complementary information by capturing emitted radiation, improving crack detection in challenging environments. Combining RGB and thermal images (RGB-T) for crack segmentation shows promise in complex real-world conditions, such as adverse weather, yet research in this area remains limited. Current RGB-T segmentation methods often fail to fully exploit the complementary relationships between modalities at various levels of interaction. To address this, we propose IRFusionFormer, a novel model for crack segmentation that effectively integrates RGB and thermal data. Our Efficient RGB-T Cross Fusion Module captures multi-scale relationships and long-range dependencies between modalities without significant computational overhead. Additionally, we introduce the Interaction-Hybrid-Branch-Supervision framework, which enhances interaction between modalities by distributing fused features across branches with joint supervision. To maintain the topological structure of cracks, we introduce a novel topology-based loss function that preserves connectivity during training. Our method achieves state-of-the-art performance, with a Dice score of 90.01% and an IoU of 81.83%, significantly improving robustness and accuracy in varying environmental conditions. These advancements address key challenges in pavement crack segmentation, offering a more reliable and efficient solution. For access to the codes, data, and models from this study, visit https://github.com/sheauhuu/IRFusionFormer

翻译：裂缝分割在土木工程中至关重要，尤其对于评估路面完整性和确保基础设施耐久性。尽管深度学习已推进了基于RGB的分割，但在低光照或运动模糊等不利条件下，其性能会下降。热成像通过捕获发射的辐射提供互补信息，从而改善了在挑战性环境中的裂缝检测。结合RGB与热图像（RGB-T）进行裂缝分割在复杂现实条件（如恶劣天气）中展现出潜力，但该领域的研究仍有限。当前的RGB-T分割方法通常未能充分利用不同交互层次上模态间的互补关系。为解决此问题，我们提出了IRFusionFormer，一种有效整合RGB与热数据的新型裂缝分割模型。我们的高效RGB-T交叉融合模块捕获了模态间的多尺度关系和长程依赖，而无需显著的计算开销。此外，我们引入了交互-混合-分支-监督框架，该框架通过将融合特征分布到具有联合监督的分支中来增强模态间的交互。为保持裂缝的拓扑结构，我们引入了一种新颖的基于拓扑的损失函数，以在训练过程中保持连通性。我们的方法实现了最先进的性能，Dice分数达90.01%，IoU达81.83%，显著提高了在不同环境条件下的鲁棒性和准确性。这些进展解决了路面裂缝分割中的关键挑战，提供了一个更可靠、高效的解决方案。要访问本研究中的代码、数据和模型，请访问 https://github.com/sheauhuu/IRFusionFormer。