Surface cracks are a common sight on public infrastructure nowadays. Recent work has been addressing this problem by supporting structural maintenance measures using machine learning methods. Those methods are used to segment surface cracks from their background, making them easier to localize. However, a common issue is that to create a well-functioning algorithm, the training data needs to have detailed annotations of pixels that belong to cracks. Our work proposes a weakly supervised approach that leverages a CNN classifier in a novel way to create surface crack pseudo labels. First, we use the classifier to create a rough crack localization map by using its class activation maps and a patch based classification approach and fuse this with a thresholding based approach to segment the mostly darker crack pixels. The classifier assists in suppressing noise from the background regions, which commonly are incorrectly highlighted as cracks by standard thresholding methods. Then, the pseudo labels can be used in an end-to-end approach when training a standard CNN for surface crack segmentation. Our method is shown to yield sufficiently accurate pseudo labels. Those labels, incorporated into segmentation CNN training using multiple recent crack segmentation architectures, achieve comparable performance to fully supervised methods on four popular crack segmentation datasets.
翻译:地表裂缝是当今公共基础设施的常见景象。 最近的工作一直通过使用机器学习方法支持结构维护措施来解决这一问题。 这些方法用来从背景中分割表面裂缝,使其更容易本地化。 但是,一个共同的问题是, 创建功能良好的算法, 培训数据需要包含属于裂缝的像素的详细说明。 我们的工作提出了一种监督不力的方法, 利用CNN分类器以新颖的方式创建表面裂缝假标签。 首先, 我们使用分类器来创建粗糙的裂缝本地化图, 使用其班级激活地图和基于补丁的分类方法, 并用基于阈值的办法来将最黑暗的裂缝像素分割成分块。 叙级器协助抑制背景区域的噪音, 通常通过标准的临界法错误地突出这些噪音。 然后, 假标签可以在端到端方法中使用, 训练标准的CNN地表裂缝断分块时使用。 我们的方法可以产生足够准确的假标签。 这些标签被并入CNNCNN的分段训练中, 使用多个最近的裂缝结构, 实现完全监控的功能。