Carrasco Miguel, Araya-Letelier Gerardo, Velázquez Ramiro, Visconti Paolo
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal las Torres 2640, Santiago 7941169, Chile.
Escuela de Construcción Civil, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.
Sensors (Basel). 2021 Nov 12;21(22):7534. doi: 10.3390/s21227534.
The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.
裂缝检测是土木工程基础设施中一项重要的监测任务，其目的在于确保耐久性、结构安全性和完整性。传统上，裂缝检测是通过目视检查来进行的，而裂缝宽度的测量则是使用裂缝宽度比较仪（CWCG）手动获取的。不幸的是，这种技术既耗时，又存在主观判断的问题，而且由于难以确保正确的空间测量（因为CWCG可能无法根据裂缝方向正确定位），所以容易出错。尽管已经开发了自动裂缝检测算法，但其中大多数都专门致力于通过深度学习技术解决分割问题，而未能解决根本问题：裂缝宽度评估，这对于土木结构的评估至关重要。本文提出了一种基于数字图像处理技术的新型表面裂缝宽度测量自动化方法。我们的方法包括三个阶段：各向异性平滑、分割以及通过k均值调整的稳定中心点，并能够对裂缝宽度和与曲率相关的方向进行表征。该方法通过评估纤维增强土建筑材料的表面裂缝来进行验证。初步结果表明，该方法在估计数字图像中的裂缝宽度方面具有鲁棒性、高效性和高精度。该方法能够有效地排除假裂缝，并检测出宽度小至0.15毫米的真实裂缝，且不受光照条件的影响。
