Image-Based Automated Width Measurement of Surface Cracking

Abstract

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.

Damage Analysis and Quality Control of Carbon-Reinforced Concrete Beams Based on In Situ Computed Tomography Tests

“…Thus, the visual appearance of a crack in a CT image may exaggerate its size, compared to the actual displacement field. For larger cracks, however, the air between the crack flanks should become more apparent and, as shown in several studies in 2D [24,25], the crack width can be estimated with greater confidence. While this method theoretically provides a valid alternative for estimating crack widths in CT reconstructions lacking temporal information, DVC should be prioritized for multi-temporal datasets, especially when dealing with cracks represented by a small number of voxels and when the structural quality of the reconstruction is sufficiently high.…”

“…Therefore, an alternative approach to concrete crack analysis is to derive crack widths from the grayscale differences of the 3D images. This method involves calculating the distances between crack edges, and it is a technique commonly used in the 2D domain [23][24][25] that can be extended to the third dimension.…”

Detection and Classification of Crack for Heritage Building

“…In [38] Carrasco et al proposed a method to determine the width of a crack with more accuracy than concerning the previous methods. The proposed method is composed of three steps as shown in Fig.…”

Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis

“…Junjie Chen and Donghai Liu [ 31 ] proposed a model for detecting damage in the water channel based on super pixel segmentation and classification and achieved an accuracy around 91%. Miguel Carrasco et al [ 32 ] discussed a methodology for measuring the width of cracks using smoothing, filtering, segmentation and estimation. The authors of [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] proposed several techniques based on CNN, pyramidal residual network for concrete crack detection, binocular vision system for pipe crack and deformation detection and also analyzed the performance of the techniques.…”