## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Method** & **Dataset** & \
\begin{tabular}{c} **Image Capturing** \\ **Technology** \\ \end{tabular} &
\begin{tabular}{c} **Number of Defect** \\ **Types** \\ \end{tabular} & **Evaluating Metrics** \\ \hline
[2] & Wavelet Multiscale & \
\begin{tabular}{c} 200 defective and 200 \\ defect-free images \\ 2766 defective and \\ 234 defect-free \\ images \\ \end{tabular} & X-ray & 4 &
\begin{tabular}{c} Success rate: 96.9\% \\ mPA: 85.13\%, mIoU: \\ 77.34\% Average \\ accuracy: 96.5\% \\ \end{tabular} \\
[14] & Concise-SSN & \
\begin{tabular}{c} 20,000 defective and \\ 100,000 defect-free \\ \end{tabular} & X-ray & 61-81 &
\begin{tabular}{c} Recall: 98.3-57.7\% \\ Precision: 96.2-93.8\% \\ \end{tabular} \\ \hline
[15] & Siamese Network & \
\begin{tabular}{c} 20,000 defective and \\ 100,000 defect-free \\ \end{tabular} & X-ray & 61-81 &
\begin{tabular}{c} Recall: 98.3-57.7\% \\ Precision: 96.2-93.8\% \\ \end{tabular} \\ \hline
[16] & \
\begin{tabular}{c} Memory-Augmented \\ GAN-Based \\ Anomaly Detection \\ \end{tabular} &
\begin{tabular}{c} 23,947 defective and \\ 2077 defect-free \\ images \\ \end{tabular} & X-ray & 21 & AUC: 87.3\% \\ \hline
[17] & \begin{tabular}{c} Feature \\ Pyramid-Based \\ Faster RCNN \\ \end{tabular} & 463 defective images & \
\begin{tabular}{c} Phase Shearing \\ Speckle Interference \\ \end{tabular} & 1 &
\begin{tabular}{c} mAP: 51.86\% AP: \\ 92\% \\ \end{tabular} \\ \hline
[18] & \begin{tabular}{c} Class-Level, \\ Weighted, Partial \\ Domain Adaptation \\ Network \\ \end{tabular} & \
\begin{tabular}{c} 500 defective and 100 \\ defect-free images \\ \end{tabular} & X-ray & 5 &
\begin{tabular}{c} Average accuracy: \\ 95.07\% \\ \end{tabular} \\ \hline
[19] & \begin{tabular}{c} Transferable Swin \\ Transformer \\ \end{tabular} & \
\begin{tabular}{c} 500 defective and 100 \\ defect-free images \\ \end{tabular} & X-ray & 5 &
\begin{tabular}{c} Average accuracy: \\ 96.17\% \\ \end{tabular} \\ \hline
[20] & \
\begin{tabular}{c} Dual-Path \\ Transformer \\ \end{tabular} & 1450 defective images & X-ray & 6 &
\begin{tabular}{c} Accuracy: 98.57\% \\ mIoU: 85.56\% \\ \end{tabular} \\ \hline
[21] & \begin{tabular}{c} Wasserstein GAN \\ \end{tabular} & \
\begin{tabular}{c} 600 defective and 200 \\ defect-free images \\ \end{tabular} & X-ray & 4 &
\begin{tabular}{c} Average \\ accuracy:95.92\% \\ \end{tabular} \\ \hline
[4] & \begin{tabular}{c} Transfer-Learning- \\ Based Methods \\ \end{tabular} & \
\begin{tabular}{c} 3366 defective and \\ 20,000 defect-free \\ images \\ \end{tabular} & X-ray & 15 &
\begin{tabular}{c} Recall: 73.7\% \\ Precision: 88 \% \\ F1\_score: 80.2\% \\ Accuracy: 94.75\% \\ \end{tabular} \\ \hline
[8] & \begin{tabular}{c} Explainable \\ Attention-Based \\ Fused CNN \\ \end{tabular} & \begin{tabular}{c} 38,710 defective and \\ 83,985 defect-free \\ images \\ \end{tabular} & X-ray & 15 & \begin{tabular}{c} Recall: 86.88\% \\ Precision: 98.5\% \\ F1\_score: 92.31\% \\ Accuracy: 95.40\% \\ \end{tabular} \\ \hline Proposed Study & \begin{tabular}{c} XAI-Powered \\ Xception and \\ Grad-CAM \\ \end{tabular} & \
\begin{tabular}{c} 2303 defective and \\ 49,198 defect-free \\ images \\ \end{tabular} & X-ray & 1 &
\begin{tabular}{c} Recall: 98.75\% \\ Precision: 99.34\% \\ F1-Score: 99.05\% \\ Accuracy: 99.19\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: This is a table caption. Tables should be placed in the main text near to the first time they are cited. reliable tires. However, it is crucial to acknowledge that our work has limitations. We only addressed one kind of tire fault because of its popularity, but future research will look into other defect types to develop a more comprehensive model. Furthermore, our future research efforts will focus on creating a comprehensive system that integrates defect detection with automated decision-making processes, with the goal of increasing efficiency and accuracy in tire manufacturing operations. R.A.A.S.