# Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera
## 1 Introduction  
Structures undergo deterioration and damage over time due to various external forces and environmental factors. Among this damage, cracks are indicators that inform us about the current state of structures well. Cracks are usually measured by visual inspection using manpower. Structures that cannot be easily accessed, such as civil engineering structures, are inspected by manpower using equipment (such as articulated ladders). The difficulty in finding cracks during inspections and the input of professional manpower increases costs and requires large amounts of time. Furthermore, in the case of the lower part of a bridge, nuclear power plant facilities, and dams, which are poorly accessible, it is hard tosecure stability because workers have to deal with poor work environments. Moreover, there are problems such as poor objectivity and errors occurring in the detection of defects due to the worker's subjective evaluation depending on their working hours, experience, personal ability, and environmental factors. In addition, for the efficient maintenance of structures, preventive maintenance is required with timely repair and reinforcement rather than one-time, large-scale reinforcement. However, frequent inspections are difficult due to limited maintenance budgets, and this makes preventive maintenance difficult. To remedy the above-mentioned problems, digital image processing (DIP) technologies using imaging equipment have been introduced, and various crack detection algorithms have been developed over the past several decades [1; 2; 3]. These technologies are expected to minimize the subjective evaluation of inspectors, thereby securing the objectivity of inspection results, and to relieve constraints on inspection timing and location, as well as solve problems such as the large manpower and cost required for existing inspections and investigations [4]. However, early studies using imaging equipment and image processing techniques used simple image processing technologies or were limited to crack information extraction techniques and had limitations in the measurement of the characteristic information (width, length, shape, etc.) of cracks due to low resolution. Early studies on image processing were mainly those that installed charge-coupled device (CCD) cameras [5], ground penetration radar (GPR), laser systems (LS), or hybrid systems (HS) [6; 7], which are classified as non-contact evaluation equipment, on a vehicle such as a van to develop image recognition equipment and equipment that inspects and measures cracks on road surfaces [8]. In related studies on image processing techniques such as ones that applied the Otsu method [9], which is based on shooting variables and threshold value settings, to concrete structures such as roads, tunnels, and bridges to detect and measure cracks [10; 11; 12; 13], image preprocessing studies have been conducted using histograms, mean values, median values, and Gaussian filters. Studies on feature extraction using morphology techniques [14; 15; 16] and studies on contour detection through primary and secondary differentiation [17; 18] have also been conducted and repeatedly developed. Thereafter, the field of applicable target structures in studies conducted on crack detection was expanded to include bridges, tunnels, and dams that cannot be easily accessed. The enhancement of the resolution of imaging equipment and the development of optical equipment has enabled the detection of cracks from long distances, which has led to studies on crack measurement. In 2012, [PERSON] detected cracks in structures and measured the widths, lengths, and directions of the cracks using an 18-megapixel camera and a 600 mm lens, and concluded that 0.2 mm cracks can be detected at a working distance of 40 m [19]. In 2014, [PERSON] secured a working distance of 60 m in a study that applied a focal length of 1000 mm using a 20-megapixel video camera, a 500 mm lens, and a 2X converter, and showed an error rate of 92.6% [20].