Crack Severity Classification from Timber Cross-Sectional Images Using Convolutional Neural Network

Abstract

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. However, this process causes internal cracks that are invisible on the outer surface. These defects are serious because they reduce the strength of the timber, i.e., the buckling strength and joint durability. Therefore, the severity of internal cracks should be evaluated. A square timber was cut at an arbitrary position and assessed based on the length, thickness, and shape of the cracks in the cross-section; however, this process is time-consuming and labor-intensive. Therefore, we used a convolutional neural network (CNN) to automatically evaluate the severity of cracks from cross-sectional timber images. Previously, we used silver-painted images of cross-sections so that the cracks are easier to observe; however, this task was burdensome. Hence, in this study, we attempted to classify crack severity using ResNet (Residual Neural Network) from unpainted images. First, ResNet50 was employed and trained with supervised data to classify the crack severity level. The classification accuracy was then evaluated using test images (not used for training) and reached 86.67%. In conclusion, we confirmed that the proposed CNN could evaluate cross-sectional cracks on behalf of humans.