## 1 Introduction  
Timber used as building material, such as cedar, goes through a drying process after being cut from the woods. This drying process primarily improves its strength. Additionally, drying is essential to stabilize the quality of timber: it prevents changes in size and shape, and the growth of bacteria. Furthermore, the weight reduction due to lower moisture content leads to lower transportation costs. High-temperature drying is the generally employed process [1]; however, it leads to significant internal cracking invisible from outside [2], as shown in Figure 1.  
Figure 1: Pictures of the internal cross-section of timber. (**a**) Timber samples with critical cracks not found from outside; (**b**) good condition dried timber.  
Cracking is a problem because it deteriorates structural performance parameters, such as buckling strength and joint durability [3,4]. Therefore, many researchers have investigated the mechanism of cracking propagation during the drying process [5,6]. Since completely preventing cracking is impractical, we need to evaluate the severity of these cracks in the timber used for construction.  
To assess the severity of internal cracks, workers check a cross-sectional view of a square piece of timber and measure the length, thickness, and area of the cracks using calipers. However, this method of examination is time- and labor-intensive. Therefore, to facilitate the automatic evaluation of internal cracks, we propose a convolutional neural network (CNN) [7,8]. CNNs are widely used in image recognition applications using artificial intelligence. If such a computational system is realized, the effort and time required for the manual inspection of timber will reduce. The aim of this study was to develop a CNN capable of classifying crack severity in lieu of a worker, as shown in Figure 2. To check timber quality, the timber specimen is first cut as shown in Figure 2a. Next, a special silver paint is sprayed onto the timber piece to highlight the cracks on the cross section. Since visual inspection is labor-intensive, as shown in Figure 2c, the CNN is used to evaluate the cracks, as shown in Figure 2d. Finally, with the proposed system, human intervention for timber inspection can be completely avoided in the future.  
Previous studies have attempted to estimate the state of invisible internal cracks from the propagation characteristics of acoustic vibrations [9, 10, 11]. Some studies employed CNNs to recognize defects in timber. A method to automatically detect cracks in wooden surfaces has been previously reported [12]. In addition, a method for the automatic detection of defects in wood, such as splits, knots, and molds, has been developed [13,14]. Unlike previous research, our study attempted to automatically evaluate the level of crack severity in a cross section of timber that has been cut and made directly visible. Additionally, we developed a CNN to detect crack severity but not defects.  
The rest of this paper is organized as follows: Section 2 describes the preparation of square cedar cross-sectional images used in this study. Section 3 describes the structure of the proposed CNN. Section 4 discusses the evaluation experiments and their results. Finally, Section 5 concludes the study with a summary and direction for future work.  
Figure 2: Schematic of the proposed system. (**a**) Timber samples; (**b**) silver-painted cross sections highlight cracks; (**c**) human inspection of crack severity; (**d**) automatic classification of crack severity using the proposed CNN; (**e**) finally, the computer will substitute the inspection task.