低光混凝土裂缝分割新方法，少样本下仍能精准定位
Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures
- 用双分支网络融合Retinex与原型学习，提升低光环境表征能力
- 仅需少量标注即达顶尖性能，在多个数据集上优于现有方法
- 适合低光环境下裂缝检测，尤其适用于隧道桥梁等场景
混凝土结构裂缝检测对基础设施安全至关重要，但真实场景中的裂缝常出现在隧道、桥底等低光环境中，导致计算机视觉分割精度下降。低光裂缝图像的像素级标注极为耗时，而多数深度学习方法依赖大量光照充足的训练数据。本文提出一种基于原型学习的少样本分割方法，融合Retinex理论与少样本学习，利用基于Retinex的反射分量实现光照不变的全局表征学习，并通过度量学习降低对大规模标注数据的依赖。引入跨相似性先验掩码生成模块，计算查询与支持特征间的高维相似性以捕捉裂缝位置与结构；设计多尺度特征增强模块，将先验掩码与多尺度特征融合，缓解空间不一致性问题。在多个基准数据集上的大量实验表明，该方法在低光条件下表现持续领先。代码已开源：https://github.com/YulunGuo/CrackFSS。
原文摘要 · Abstract (English)
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.
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