## 1 Introduction  
Cedar and cypress used for wooden construction have high moisture content after harvesting. To be used as building materials, they must undergo high-temperature drying [1]. This process is indispensable to prevent deformation and mildew. However, if the timbers are not dried using the appropriate procedure, internal cracks are formed, as shown in Figure 1a [2]. Internal cracks are severe because they reduce the durability of timber [3, 4]. Based on this knowledge, physical stress propagation during drying was analyzed to improve the high-temperature drying technique with less cracking [5, 6]. In the actual field, severe internal cracks can occur because of excessive high-temperature drying. As shown in Figure 2a, the quality manager extracted a sample block of timber. In Figure 2b, the quality manager had to decide whether to use the material for the building based on the severity of the cracks by measuring the crack length, width, and number using calipers. However, this task is labor-intensive. Therefore, this study aims to realize an intelligent system that automatically evaluates the severity of internal cracks based on cross-sectional images using a convolutional neural network (CNN) [7], as shown in Figure 2c. We developed a simple CNN dealing with silver-painted cross-sectional images to make the cracks more clearly visible, as shown in Figure 3a [8]. The simple CNN that we proposed in our previous article could evaluate crack severity with approximately 85.67% accuracy [9]. However, the silver-painting task is cumbersome. Thus, in the present study, the proposed CNN deals with unpaired images, as shown in Figure 3b. Figure 1: Images of cross-sections of cedar square timbers. (**a**) Example of internal cracks; (**b**) No cracks without risk to physical strength. Figure 2: Process of substituting a human with a convolutional neural network (CNN) to evaluate timber crack severity. (**a**) Sample block extraction; (**b**) Human evaluation is burdensome; (**c**) CNN evaluation of crack severity. In previous studies related to the internal cracks in the timber, we found attempts to infer the internal cracks using acoustic signals [10; 11; 12]. However, our objective differs compared to these studies because they attempt to infer the condition of invisible internal cracks. In contrast, our attempt is to evaluate the visible cross-section depending on the crack severity. Other studies applied CNNs to automatically detect cracks on wooden building surfaces [13] in order to find critical damage in wooden buildings. We also found studies to find cracks, knots, and mold in timbers [14; 15] using CNN. Meanwhile, our study differs because we aimed to automatically evaluate crack severity levels rather than to detect cracks. In other words, most of the relevant studies focused on the automatic detection of cracks and not on the evaluation of the level of severity in the detected cracks. The present paper differs from other similar studies in that it focuses on evaluating the rank of the timber's quality. We have not found any study that attempts to employ a CNN to evaluate the quality of square timber considering cracks. Quality control staff in vendors, and researchers improving high-temperature drying techniques, have to rank many square timbers visually, and the burden of this task is considerable. The practical application of this study is to replace humans with artificial intelligence that automatically evaluates the quality of timbers. Our previous study [8; 9] constructed a simple CNN to evaluate silver-painted cross-sectional grayscale images. In the painted image, only the cracks are visible, excluding annual rings or knots. However, the painting process is labor-intensive. Therefore, the present paper targets color images without silver paint, and we employed a CNN taking color images as the input. Convolutional neural networks are more suitable for capturing image features than a classical fully connected neural network because the CNN focuses on the relations of neighboring pixels of an image.