Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms

Abstract

The presence of concrete cracks in structures indicates possible structural deterioration, but it is quite difficult to measure crack width accurately. While much research has been conducted on crack detection using image processing, there is a gap in the accurate quantification of crack width in millimeters. Current methods either measure in pixels or require the attachment of scales or markers onto a measured surface, which can pose safety hazards in hard-to-reach areas. This paper addresses these issues by proposing a novel image-based method for measuring concrete crack width in millimeters using a laser beam and image processing. The proposed method was validated in the laboratory by capturing images of concrete cracks with two cameras of different resolutions, each attached with lasers. The lasers projected a circular laser beam onto the surface of the concrete under inspection. The images were then pre-processed, segmented, and skeletonized for crack width measurement in pixels. The relationship between the laser diameter and camera distance from the surface was used to convert the measured crack width from pixels to millimeters. The method was performed with high accuracy, as indicated by the absolute error. The largest absolute error was 0.57 mm, while the smallest absolute error was 0.02 mm. The proposed method allows real-world interpretation of results in millimeters. As a result, measured crack widths can easily be compared to allowable limits in international standards, which are typically expressed in metric or SI units. The proposed method can also promote safer inspections in areas of low accessibility by attaching the laser to devices such as drones.

Intricacies of Opening Geometry Detection in Terrestrial Laser Scanning: An Analysis Using Point Cloud Data from BLK360

“…Compared to photogrammetry, using TLS for crack detection is superior, especially for assessing crack depths. Laefer et al [18] present the fundamental mathematics and experimental validation for determining the minimum detectable crack width in unit-based masonry using TLS. However, guaranteeing the integrity of data remains a challenge, especially when detecting intricately structured cracks [19,20].…”

Deep Learning for Concrete Crack Detection and Measurement

“…Damage is defined as material or geometric changes that occur to the material of structure [3,4]. Cracks are often the earliest indication of damage in concrete structures [5,6], and undetected cracks can worsen over time, leading to reduced structural integrity and potentially catastrophic failures, resulting in injury, loss of life and huge economic costs [7,8]. The width of a crack typically indicates the severity of damage.…”

“…Typical image processing algorithms for image-based concrete crack detection include thresholding [9][10][11], edge detection [12,13] and binarization [14][15][16]. Morphological features, such as thinning, closing, opening, erosion and dilation, have also been commonly used to improve the results of image processing algorithms [5,16,17]. Crack detection using image processing has produced satisfactory results; however, its accuracy is, due to the need for manual parameter selection, limited by the user's expertise.…”

“…Some studies [36][37][38][39][40][41] have found ways to measure concrete crack width in millimetres by devising methods to carry out pixel to millimetre conversion. However, Nyathi et al [5] found that these methods require knowledge of key parameters that may not always be readily available, such as focal length, camera resolution, number of pixels along the long side of image sensor, etc. Other methods used physical markers attached to the measuring surface to convert pixels to millimetres.…”

“…Although the conversion of pixels to millimetres was successfully achieved, a new challenge of imposed safety risks was introduced, especially in hard-to-reach areas. To overcome these challenges, we previously developed a novel laser calibration method [5] for converting pixels to millimetres by using a laser beam. While the method performed well, achieving a mean absolute error of 0.26 mm, it was limited, both by the use of image processing techniques for crack segmentation and the angle at which images were captured.…”