  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.
