# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 1 Introduction  
Concrete structures are increasingly experiencing deterioration due to various factors such as aging, increased traffic loads, and extreme weather conditions [1, 2]. While concrete structures experience various kinds of damage, cracks are typically one of the most common indicators of the presence of damage. The presence of cracks does not always indicate imminent collapse; however, they often lead to reduced structural integrity. Cracks weaken structures by leading to issues such as corrosion of internal rebar and reduced loading capacity [3]. It is important to detect and measure cracks in a timely manner in order to infer decisions on necessary repairs and maintenance [4]. Failure to do so can lead to catastrophic failures with a high cost to human life and the economy [5]. It is worth noting that it is common for concrete to crack, and not all cracks are a cause of concern. The width of cracks is typically a good indicator of the severity of the cracks. The conventional method for detecting and measuring concrete cracks has been through visual inspections performed by trained professionals on-site. However, this method has limitations such as a potential for human error, high costs of operation, disruptions to traffic, and access limitations [2, 6]. Crack widths are typically recorded manually during the visual inspection using crack-width comparator gauges [7], crack scales, or specialized bridge inspection vehicles. Extensive research has been conducted on image-based crack detection methods. The images of the concrete surfaces are captured using a variety of image acquisition devices. The most common devices include DSLR cameras [6; 8; 9; 10], unmanned aerial vehicles [6; 8; 11; 12], and smartphones [9; 13; 14]. Other techniques include infrared cameras, 3D laser microscopes [15], and stereovision [16]. Early works looked at the use of image-processing techniques that used threshold-based methods [17]. Threshold-based methods rely on pixel intensity information for the detection of cracks. [PERSON] and [PERSON] [18] proposed a method for the detection of cracks on noisy concrete surfaces which applied locally adaptive thresholding to an image that had undergone pre-processing and probabilistic relaxation. [PERSON] et al. [19] detected cracks using a hybrid image processing method that used the image binarization technique by [PERSON] [20]. The technique was initially designed for detecting text in noisy backgrounds. The detected cracks were then skeletonized, allowing crack width to be measured as the distance between the two extreme ends of the crack skeleton. [PERSON] et al. [21] also used binarization and skeletonization based on medial axis transform and the Canny edge detection algorithm for crack detection and measurement. [PERSON] et al. [4] measured crack widths in pixels by using a medial axis transform for the skeletonization of cracks. Thus, representing the cracks as single-pixel wide crack representations. The use of morphological operations prior to skeletonization is a widely adopted technique in image-based concrete crack detection. Common morphological operations include thinning, edge detection, closing, opening, erosion, and dilation. This step is often applied to improve the accuracy of the results [4; 19]. Other skeletonization algorithms have been proposed, such as the Hilditchs approach [22] and the 3D medial axis thinning algorithm [23; 24]. However, these methods tend to be computationally demanding due to their iterative nature [4]. The majority of existing studies in the literature measure concrete crack width in pixels and do not provide a direct understanding of the extent of damage in the real world. Recent studies have explored methods for measuring concrete crack width in millimeters from images. [PERSON] et al. [25] measured the change in crack width in millimeters by keeping the distance between the concrete surface and the image scanner of known resolution constant. The estimated standard uncertainty of average crack width was found to be \(\pm\)0.008 mm.