Crack detection is critical for concrete infrastructure safety, but real-world cracks often appear in low-light environments like tunnels and bridge undersides, degrading computer vision segmentation accuracy. Pixel-level annotation of low-light crack images is extremely time-consuming, yet most deep learning methods require large, well-illuminated datasets. We propose a dual-branch prototype learning network integrating Retinex theory with few-shot learning for low-light crack segmentation. Retinex-based reflectance components guide illumination-invariant global representation learning, while metric learning reduces dependence on large annotated datasets. We introduce a cross-similarity prior mask generation module that computes high-dimensional similarities between query and support features to capture crack location and structure, and a multi-scale feature enhancement module that fuses multi-scale features with the prior mask to alleviate spatial inconsistency. Extensive experiments on multiple benchmarks demonstrate consistent state-of-the-art performance under low-light conditions. Code: https://github.com/YulunGuo/CrackFSS.

翻译：裂缝检测对混凝土基础设施安全至关重要，但现实中的裂缝常出现在隧道、桥底等低光照环境中，这会降低计算机视觉分割的准确性。低光照裂缝图像的像素级标注极为耗时，而大多数深度学习方法需要大量光照良好的数据集。我们提出了一种双分支原型学习网络，将Retinex理论与少样本学习相结合，用于低光照裂缝分割。基于Retinex的反射分量引导光照不变的全局表征学习，而度量学习减少了对大规模标注数据集的依赖。我们引入了交叉相似性先验掩码生成模块，通过计算查询特征与支持特征间的高维相似性来捕捉裂缝位置与结构；以及多尺度特征增强模块，将多尺度特征与先验掩码融合以缓解空间不一致性。在多个基准数据集上的大量实验表明，本方法在低光照条件下持续取得了最先进的性能。代码：https://github.com/YulunGuo/CrackFSS。