## 4 Experimental Results  
The dataset was split into 90% for training and 10% for testing according to the standard splitting ratio for large datasets [24]. All performance results of the model's evaluation for the 10% validation set are presented in Table 3. The performance evaluation criteria used are accuracy, precision, recall, and F1-score. These metrics were used as defined in [25; 26]. From the table, we can observe that the model successfully achieved an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and an F1-score of 99.05. These results show that the model is capable of detecting and classifying the defective tires from the non-defective ones. The experimental setup with the convergence rate plots was established, adding recall, precision, and F-score for the training and testing (validation) datasets. The convergence curves, which are a graphical representation that depicts the progression of the loss and accuracy over successive epochs during the training of the model, are illustrated in Figure 4. The curve illustrates the effectiveness of training and highlights potential issues like overfitting that might arise during the training process, providing an initial view of the model's performance [27]. The curves summarize that through training epochs, the model shows a steady increase in accuracy and a decrease in loss, signifying that the model is learning and adapting to the patterns in the data. As the model approaches the final epochs of training, the curves start to display a slight upward slope with a small increment in accuracy and decrement in loss. During the validation stage, on the other hand, we observed some fluctuations in the model's performance with the validation set as it trained. This behavior is typical, indicating that the model starts to capture significant features from the data and is transitioning from a state of randomness to one of structured learning. The number of epochs was limited to 30 epochs since we observed no further improvements in the model's performance. The classification results of the proposed model are depicted in Figure 5 as a confusion matrix. The positive class is considered the defective tires, and the negative class is the non-defective tires. As the confusion matrix illustrates, 3639 defective tires were correctly classified out of 3685, with 46 being misclassified as non-defective. Similarly, 4897 non-defective tires were correctly classified out of 4921, with only 24 misclassifications. In this study, when defect-free tires were found to be defective, they could be re-examined and found to be defect-free. However, if defective tires are found to be defect-free, factories can put them on sale. It is therefore important to see that 3639 of the 3685 defective tire samples were classified correctly. This indicates that the model can minimize significant errors in real-world scenarios. Furthermore, the model's success in correctly identifying defect-free tires is noteworthy. For example, 4897 out of 4921 defect-free tire samples were \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Prediction Result** \\ \hline Accuracy & 99.19\% \\ Recall & 98.75\% \\ Precision & 99.34\% \\ F1-Score & 99.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance results of the proposed model. correctly classified, indicating that the model is generally robust in correctly distinguishing defect-free samples. Figure 4: Convergence rate of the proposed method. Figure 5: Confusion matrix of the proposed model. In Figure 6, we observe heatmaps of two tires, one that is non-defective and the other that is defective. These heatmaps show a prominent and vibrant red area around a specific region of the tire, precisely where the tire's sidewall and tread layer meet. This area is defined as a point where features that may be decisive in terms of the structural integrity or performance of the tire are located.