标题： 粗粒度到细粒度裂缝线索用于鲁棒裂缝检测 显示英文标题

标题： Coarse-to-fine crack cue for robust crack detection

摘要： 裂缝检测是计算机视觉中的重要任务。 尽管在数据集内的性能令人印象深刻，基于深度学习的方法在推广到未见过的领域时仍然存在困难。 裂缝的细结构特性通常被之前的方法所忽视。 在这项工作中，我们引入了CrackCue，一种基于从粗到细的裂缝线索生成的稳健裂缝检测新方法。 核心概念在于利用细结构特性生成稳健的裂缝线索，指导裂缝检测。 具体来说，我们首先对裂缝图像进行简单的最大池化和上采样操作。 这会产生一个粗略的无裂缝背景，通过重建网络可以得到一个精细的无裂缝背景。 原始图像与精细无裂缝背景之间的差异提供了一个精细的裂缝线索。 这个精细线索嵌入了不受复杂背景、阴影和不同光照影响的稳健裂缝先验信息。 作为一个即插即用的方法，我们将提出的CrackCue集成到三种先进的裂缝检测网络中。 大量实验结果表明，所提出的CrackCue显著提高了基线方法的泛化能力和鲁棒性。 源代码将公开可用。 显示英文摘要

摘要： Crack detection is an important task in computer vision. Despite impressive in-dataset performance, deep learning-based methods still struggle in generalizing to unseen domains. The thin structure property of cracks is usually overlooked by previous methods. In this work, we introduce CrackCue, a novel method for robust crack detection based on coarse-to-fine crack cue generation. The core concept lies on leveraging the thin structure property to generate a robust crack cue, guiding the crack detection. Specifically, we first employ a simple max-pooling and upsampling operation on the crack image. This results in a coarse crack-free background, based on which a fine crack-free background can be obtained via a reconstruction network. The difference between the original image and fine crack-free background provides a fine crack cue. This fine cue embeds robust crack prior information which is unaffected by complex backgrounds, shadow, and varied lighting. As a plug-and-play method, we incorporate the proposed CrackCue into three advanced crack detection networks. Extensive experimental results demonstrate that the proposed CrackCue significantly improves the generalization ability and robustness of the baseline methods. The source code will be publicly available.