CrackNex：一种面向无人机巡检的基于Retinex理论的小样本低光照裂缝分割模型
CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections
AI总结：
针对低光照场景下裂缝分割精度低、标注数据不足的问题，提出基于Retinex理论的小样本分割框架CrackNex，通过融合支持与反射率原型学习光照不变表征，性能超越现有最优方法，还发布了首个低光照裂缝分割基准数据集LCSD。
摘要：
混凝土结构的常规目视检测对维护关键基础设施的安全与完整性至关重要。这类目视检测有时会在低光照条件下开展，例如桥梁健康检查。由于裂缝与周围环境对比度低，此类场景下的裂缝分割极具挑战性。然而，大多数深度学习方法都是针对光照充足的裂缝图像设计的，因此在低光照场景中性能会急剧下降。此外，传统方法需要大量标注好的低光照裂缝图像，耗时耗力。本文针对这些挑战提出了CrackNex框架，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光照不变表征。此外，我们采用小样本分割来解决训练数据效率低下的问题。在CrackNex中，我们从支持集中提取支持原型和反射率原型，随后设计了原型融合模块来整合两种原型的特征。CrackNex在多个数据集上的表现优于SOTA（当前最优）方法。另外，我们提出了首个低光照裂缝分割基准数据集LCSD，该数据集包含102张光照充足的裂缝图像和41张低光照裂缝图像。数据集与代码可在https://github.com/zy1296/CrackNex获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
