## 2 Related Research  
The technology of estimating real distance using an image is utilized in various fields of computer vision, such as autonomous driving. Typically, laser scanning provides high-accuracy location information, but its application in real environments is expensive. To solve this issue, a stereo-matching method was used to estimate the location of objects from acquired images using two or more cameras. The stereo matching method has been extensively studied in the field of computer vision, and studies were conducted to use a calibration process to make two cameras in the same parallel operate in a stereo manner [2; 3; 4; 5]. However, cameras used for stereo imaging have different postures and different focal lengths. In addition, there are differences in brightness according to the lighting of the two places where the camera is installed, or differences in brightness in an image according to the exposure of the aperture. In addition, various conditions cause problems that are difficult to correct [6]. To address these problems, many studies [7; 8] have been conducted. In particular, an algorithm based on a convolutional neural network (CNN) [7] was presented, and various stereo matching algorithms were announced [4; 8; 9]. Underground utility tunnels are narrow and long security facilities, and it is difficult to install additional cameras for stereo matching, which is a method for estimating depth. Therefore, in this study, depth-estimation research using a single camera was investigated. The stereo matching method is a system that imitates the human eye and can estimate depth information from monocular cues, such as shadow and texture, using only a single image [10]. Although studies have been conducted to estimate depth through object outlines [11], segments, and shadows [12; 13], reliable depth-estimation information cannot be obtained because it relies on several clues and rules. Numerous studies have been conducted to estimate depth with only a single camera using a CNN. CNN-based single-image depth-estimation research was first proposed by [14], and neural networks were used to precisely improve approximate depth information. Various new methods have been proposed to improve the performance of single-image depth estimation [15; 16]. Studies using the regression loss function to improve the speed and performance of learning models [17] and studies on modeling methods for more accurate mapping of unclear mapping relationships between single images and depth maps using residual learning [18] have been conducted. New methods have been presented for single-image depth estimation. In addition, a method of improving loss function for faster in-depth estimation accuracy and quality-improvement learning based on transfer learning was proposed [19]. Most depth estimation algorithms are based on supervised neural networks, but research has been conducted to address the disadvantages of the existing unsupervised depth estimation methods by introducing a knowledge-distillation-based approach [20]. Numerous studies have been conducted proposing a method for measuring distance using a survey method for single-image depth estimation without applying a CNN model. A study [21] was conducted using triangulation for indirect estimation of the distance to an object based on known planes and angles, without direct measurement. As depth estimation via a single image is computationally expensive, a study was conducted to match the relationship between the image projection point and the corresponding point [22]. It tracked the new absolute position, proposed an error-correction algorithm, and presented a model that maps 3D spatial points to 2D images. Although studies on various single-image depth-estimation methods have been conducted, only a few studies have directly compared, tested, and applied them to the actual field. In this study, among the single-image depth-estimation methods, methods applicable to CCTVs installed in underground utility tunnels were selected and compared.