## 1 Introduction
[15] conducted research on the detection of concrete cracks based on computer vision using U-Net and found it to be more robust and effective compared to CNN-based methods. [PERSON] et al. [16] conducted research on the detection and quantification of concrete cracks using deep learning and structured light. [PERSON] et al. [17] proposed a methodology for the detection and monitoring of concrete cracks in a concrete material sample and specimen using deep learning-based multi-resolution analysis. [PERSON] et al. [18] conducted research on deep learning-based concrete crack detection using hybrid image scanning that combines vision and infrared thermography images. [PERSON] et al. [19] developed a deep learning-based image analysis technique for crack detection and feature analysis in small-scale infrastructure images. If safety inspections are conducted based on these image-based preceding studies, it becomes possible to enhance the objectivity of the inspection results by minimizing the subjective intervention of the inspector. Additionally, it can improve the limitations on inspection time and location, as well as enhance the existing inspection and investigation processes. This, in turn, is expected to reduce manpower and costs associated with inspections. However, most of the safety inspection studies of image-based facilities are mainly focused on crack detection. It is related to the presence and detection of cracks using the development of crack detection algorithms and deep learning-based models, and research on crack location estimation is relatively insufficient. The location information of cracks is a crucial factor in conducting effective safety inspections of actual facilities. It enables the understanding of crack size, shape, and distribution, which are vital for implementing appropriate maintenance and reinforcement measures [20]. However, as mentioned earlier, most of the research related to cracks has focused on their identification and representation, while studies on crack location estimation are relatively scarce or nonexistent. [PERSON] et al. [21] conducted a study on the identification and localization of concrete bridge defects based on D-CNN and transfer learning. The trained model for defect identification achieved a high accuracy of 97.13%, but for defect localization, it provided only an approximate pixel-level location. [PERSON] et al. [22] evaluated concrete cracks using two stereo visions with different focal lengths. A crack was detected using a framework based on the crack candidate region, and the location of the crack was simply expressed via the construction of a 3D model using the structure from the motion process. Studies related to such crack location information have also focused on crack detection and identification, and the crack location information has been shown to be at the approximate location expression level. [PERSON] et al. [23] proposed a methodology for estimating the location of concrete cracks with an accuracy at the level of millimeters using image processing techniques that utilize a reference object. However, a limitation of this methodology is that it cannot be applied when there are no identifiable reference points in the facility for defining the reference object for estimating the location of concrete cracks. Moreover, it was also found that there exists a limitation in the accuracy of the image processing technique when there is an inadequate number of reference objects available as feature points in the image. The purpose of this study is to address the limitations of previous crack location estimation research based on image processing techniques that utilize reference objects. It aims to conduct a new image-based concrete crack location estimation study, seeking improvements in the existing methods.