# A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software
## 3 Results and Discussion  
Table 2 displays the results of the defect detection. Green cells labelled with + signs indicate that the defect has been precisely identified. Red cells labelled \(\mathrm{D}-\) indicate that the software detected a defect that does not exist, whereas red labelled \(\mathrm{ND}-\) means the software failed to identify an existing defect. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**Software 1**} \\ \cline{2-7}  & \multicolumn{4}{c}{**Defects**} \\ \cline{2-7}  & **Crack** & **Knot** & **Sapwood** & **Ingrown bark** & **Ambrosia wood** & **Wood ray** \\ \hline
1 & \multicolumn{4}{c}{**-**} & \multicolumn{4}{c}{**-**} & \multicolumn{4}{c}{**-**} & \multicolumn{4}{c}{**-**} \\ \hline  
\hline \end{tabular}
\end{table} 
Table 2: Results of defect detection on the wood surface. The percentages of successful software defect detection are given in Table 3. Detected defects that did not exist (D \(-\)) but were still determined by software were not included in the percentage calculation. Those errors can be explained by the software identifying the color difference (early and late wood) as a defect. Regarding crack detection, both software have identical levels of recognition (65% vs. 75%), with software 1 being slightly better. A study [36] that utilized the classification and regression tree approach (CART) found the final recognition rate of cracks and pinholes to be up to 96.3%. The study, which explores the application of vibrothermography in detecting cracks in parquet lamellae [37], achieved an accuracy of 80% by utilizing completed local binary pattern histograms to capture the texture image and employing background suppression and thresholding techniques for crack segmentation. In an alternative approach, researchers have developed an identification algorithm that employs the local binary pattern (LBP) and a local binary differential excitation pattern on birch veneer. This method effectively detects cracks and mineral line defects [38]. The study's findings indicate that the algorithm improves capability in detecting cracks and mineral lines, as \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Software 2**} \\ \cline{2-7}  & **Crack** & **Knot** & **Sapwood** & **Ingrown bark** & **Ambrosia wood** & **Wood ray** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 3: Software defect detection percentage of all selected defects. evidenced by a precision rate of 0.943. Overall, the percentage of defect detection with both software was relatively low. The percentage of knot detection was also only slightly higher with software 2 (77.78% vs. 70.37%). Under this appellation, all knots, regardless of health, size, or shape, are included. In a study conducted in 2020 [39], machine learning techniques were employed to classify knots and cracks in oak, spruce, and TMT spruce-sawn timber within the wood industry. The findings indicated that a support vector machine (SVM) attained a defect identification accuracy of 75.8%. Furthermore, the k-nearest neighbors (k-NN) algorithm and the decision tree approach achieved accuracies of 74.2% and 71.9%, respectively. The previously mentioned analysis [36] showed that the level of recognition of knots (sound and dead) is, on average, about 90% for the wood species Xyloma congestum. The majority of researchers focused on knots, as they are the most commonly encountered defect in timber, influencing both the structural integrity of the wood and the overall quality of the final product [39]. Additional improvements could be attained comparing the results obtained with the researched software. According to a study conducted in 2010 [40], sound and pin knots generally do not significantly influence the mechanical properties of wood in most applications. On the other hand, dead knots and knot holes appear to have the opposite effect.