## 1 Introduction
An illustration of the reference photos is provided in Figure 2. While comparison with a reference sample is a simple avenue to evaluate surface quality, some of the views expressed in that connection assert that it can be problematic because of the variation between the different printed scales of the reference samples and human eyes. Moreover, one surface may have several types of bugholes together or a combination of several different surface imperfections, so using the references becomes difficult and somewhat subjective [14; 16; 17; 18]. Figure 1: Data collection scheme and example using the Quality Surface Index (QSI) approach: Each colored circle represents a specific pore diameter [11]. Due to these quantitative problems, more objective detection and evaluation methods have been developed. Image processing technology (IPT) is considered a powerful automated tool for application in to civil engineering materials that can deliver objective results in many areas of study [20; 21], such as in concrete bridge inspection [22], the classification of radar images [23], and crack monitoring and quantification for concrete surfaces [24]. There are several methods focused on the use of image processing technology to detect and evaluate the bughole distribution on concrete surfaces [5; 10; 25; 26; 27; 28]. These methods allow one to delineate and quantify areas with surface defects according to the preset quality parameters introduced into the control software [11]. However, there are several deficiencies associated with the direct use of image processing technology for surface defect inspection. Although these methods reduce the subjectivity of the results with image processing algorithms that are designed to assist inspectors in detecting defects, their final results depend heavily on human judgment [29]. In addition, the technical variables for each concrete element (luminosity, brightness, roughness, geometry, etc.) must be correctly analyzed before they are introduced to obtain results that correspond to reality, which impedes their application [11; 30]. The detection results of image processing algorithms may be inexact due to the impact of noise, such as illumination, shadows, and combinations of several different surface imperfections [31; 32; 33; 34]. Finally, these methods frequently involve the need for several steps; most of them can be repeated on several occasions until the desired results are obtained (trial and error). In other methods, analysis and inspection are more complicated, as in the one method proposed by [PERSON] et al. [18], where an advanced 3D scanner and an optical method were used to analyze and measure the surface topography of a concrete surface. In recent years, other inspection methods were developed based on deep learning algorithms [35]. Deep learning algorithms have demonstrated outstanding technical capabilities in the analysis of inspected images [36; 37; 38; 39]. In deep learning, convolutional neural networks (CNNs) are specially constructed to treat the variability in 2D forms. As such networks prevent the complex preprocessing of an image, it is shown that they surpass all other techniques. These characteristics make CNNs an effective identification method that is widely used [29; 35; 40; 41]. These methods find widespread use in the detection of concrete surface cracks [42; 43; 44; 45; 46] and bugholes on concrete surfaces [9; 18]. However, errors may occur during image tests when there are several types of defects, including cracks, bugholes, and color differences. Thus, it becomes challenging to make a clear distinction between bugholes and some areas of darker color difference, which causes a misunderstanding coming from some similarities in their form. Additionally, due to the light color of bugholes with a small depth, the contrast of colors with the concrete surface is not evident [36; 43] because they are usually darker than the remaining concrete surface.