Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network

Measuring the drying checks requires a lot of time and effort, and the judgment of the drying checks differs depending on the person. Semi-automatic measurement methods based on image processing in previous studies required special treatment of specimens, and has not solved the problem of time-consuming. In this study, to develop a convenient measurement method to quantify drying checks in sawn timber, we created a Convolutional Neural Network (CNN) that can discriminate drying checks in cross-sectional images pixel by pixel. The CNN was trained on 850 cross-sectional images (cross-sectional dimension 105×105 mm) of sugi boxed-heart square timber acquired by a flatbed scanner, and a testing set of 150 images evaluated its performance. The results showed that the Root Mean Squared Error (RMSE) for total check area and total check length were 11.8 mm 2 and 28.4 mm, respectively. In especially, the area was found to be predictable with high accuracy. The low accuracy of length prediction compared to area was attributed to the loss of small checks information when the images were compressed and input into the CNN. It was necessary to change the method of inputting images to the CNN to improve the prediction accuracy.

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

Kato

Wada

Shiogai

et al. 2023

Applied Sciences

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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.

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Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network

Cited by 3 publications

References 17 publications

Advances in wood drying research and development

Advances in wood drying research and development

A Quantify Method for Drying Checks of Sugi Based on Image Analysis Using Deep Learning

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

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