The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by combining previously available datasets and unifying the annotations by tackling the inherent problems within each dataset, such as noise and distortions. We also present a pipeline that combines Image Processing and Deep Learning models. Finally, we benchmark the results of proposed models on these metrics on our new dataset and compare them with state-of-the-art models in the literature.
翻译:发现裂缝是监测结构健康和确保结构安全的一项关键任务。人工发现裂缝的过程对检查员来说是耗时和主观的。一些研究人员已经尝试使用传统的图像处理或学习技术来解决这个问题,然而,他们的工作范围仅限于探测单一类型表面(墙壁、人行道、玻璃等)的裂缝。评估这些方法所使用的衡量标准也因文献而异,使得比较技术具有挑战性。本文件通过将以前已有的数据集结合起来,并通过处理每个数据集中固有的问题(例如噪音和扭曲)来统一说明来解决这些问题。我们还提出了一个将图像处理和深层学习模型结合起来的管道。最后,我们将这些模型的结果以我们新的数据集作为基准,并将这些模型与文献中的最新模型加以比较。