Routine visual inspections of concrete structures are imperative for upholding the safety and integrity of critical infrastructure. Such visual inspections sometimes happen under low-light conditions, e.g., checking for bridge health. Crack segmentation under such conditions is challenging due to the poor contrast between cracks and their surroundings. However, most deep learning methods are designed for well-illuminated crack images and hence their performance drops dramatically in low-light scenes. In addition, conventional approaches require many annotated low-light crack images which is time-consuming. In this paper, we address these challenges by proposing CrackNex, a framework that utilizes reflectance information based on Retinex Theory to help the model learn a unified illumination-invariant representation. Furthermore, we utilize few-shot segmentation to solve the inefficient training data problem. In CrackNex, both a support prototype and a reflectance prototype are extracted from the support set. Then, a prototype fusion module is designed to integrate the features from both prototypes. CrackNex outperforms the SOTA methods on multiple datasets. Additionally, we present the first benchmark dataset, LCSD, for low-light crack segmentation. LCSD consists of 102 well-illuminated crack images and 41 low-light crack images. The dataset and code are available at https://github.com/zy1296/CrackNex.

翻译：混凝土结构的常规目视检查对于维护关键基础设施的安全性和完整性至关重要。此类目视检查有时需要在低光照条件下进行，例如桥梁健康检测。在此类条件下进行裂纹分割具有挑战性，因为裂纹与其周围环境的对比度较低。然而，大多数深度学习方法专为良好光照下的裂纹图像设计，因此在低光照场景下性能急剧下降。此外，传统方法需要大量标注的低光照裂纹图像，这十分耗时。本文通过提出CrackNex框架来解决这些挑战，该框架利用基于Retinex理论的反射率信息帮助模型学习统一的照明不变表示。同时，我们采用少样本分割来解决训练数据不足的问题。在CrackNex中，从支持集中提取支持原型和反射率原型。随后设计了一个原型融合模块来整合两个原型的特征。CrackNex在多个数据集上的表现优于现有最先进方法。此外，我们提出了首个低光照裂纹分割基准数据集LCSD。LCSD包含102张良好光照裂纹图像和41张低光照裂纹图像。数据集和代码可在https://github.com/zy1296/CrackNex获取。