# Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera
## 1 Introduction
[PERSON] measured virtual images of 0.4 to 2.0 mm wide cracks using a 17-megapixel imaging device while changing the working distance from 725 to 1760 mm. He analyzed the filmed images and reported that increases in the working distance led to increases in the ground sample distance (\(GSD\)) of the images, that the accuracy of crack width measurement decreased as the number of pixels representing cracks decreased, and that to enhance the accuracy of crack measurement, the working distance should be reduced, the focal length should be increased, and the resolution should be enhanced [21; 22]. [PERSON] proposed a crack detection and measurement algorithm to automatically measure cracks and measured the crack widths of experimental subjects and actual structures. The measured crack sizes ranged from 1.22 mm to 30 mm and error rates of 7-8% were shown [23]. [PERSON] used 12-megapixel imaging equipment to measure 15.1 mm long cracks. He proposed a morphological operator-based algorithm and made efforts to reduce measurement errors [24]. Most crack measurement studies in the past dealt with cracks greater than 0.5 mm, and studies on microcracks smaller than 0.3 mm have been limited. Recently, studies that linked unmanned aerial vehicles (UAV) [25; 26] with machine-learning-based image processing methods such as neural networks [27; 28] to detect or measure cracks in concrete structures have been steadily increasing. Various kinds of imaging equipment are applied according to the operational limits of unmanned aerial vehicles. To acquire crack information, the pixel size of the video image according to the working distance must be known. However, the resolution of imaging equipment and optical equipment mounted on general unmanned aerial vehicles and the precision of GPS cause limitations in acquiring crack information, and related studies are insufficient. In this paper, an indoor measurement experiment was conducted to measure cracks in concrete structures and present guidelines for the image acquisition and processing of imaging equipment. Two units of DSLR video equipment, which is applied in various fields, were selected, and both were equipped with full-frame image sensors. In addition, the focal length was changed using 50 mm, 100 mm, and 135 mm lenses. In order to derive the crack measurement limits and optimal working distances of the selected imaging equipment and optical equipment to analyze precision according to differences in resolution, shooting was conducted while changing the working distance at intervals of 10 mm. In the experiment, virtual images equivalent to microcracks not larger than 0.3 mm were fabricated, and the images were acquired. The pixel values of these images were measured with [PERSON]'s image processing technique through a preprocessing process, and the areas of the virtual images were obtained by applying the \(GSD\) values according to the working distance. The effects of the pixel values measured through the respective imaging equipment and optical equipment on the measurement of crack width were analyzed with theoretical and experimental approaches.