Evaluation of the Timber Internal Crack Using CNN

When freshly harvested, cedar and cypress contain a high amount of moisture and must undergo a high-temperature drying process before we use them as building materials. However, a high-temperature drying process could cause internal cracks in the wood, and these defects reduce joint strength and buckling resistance. Therefore, human experts must visually evaluate the severity of cracks in the cross-section of timbers, which is highly labor-intensive and time-consuming. To address this issue, the authors have proposed to employ a convolutional neural network (CNN) to automatically evaluate the severity of cracks from cross-sectional images of timbers. Our previous study demonstrated that the proposed CNN could appropriately evaluate the crack severity. However, since the number of images was only 64, employing more images was required for further validation. Therefore, the authors added 140 images to validate the CNN in the present paper. This paper describes the experiment in detail and discusses the findings and future works in the conclusion part.