## 4 Experiments
### Experiments' Results
#### 4.4.2 Quantitative Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Image Type**} & \multirow{3}{*}{\
\begin{tabular}{c} **[PERSON] et al.** \\ [15] \\ \end{tabular} } & **[PERSON] and [PERSON]** & \multicolumn{2}{c}{**SIFT-Based**} & \multicolumn{2}{c}{**[PERSON]**} & \multirow{2}{*}{
\begin{tabular}{c} **[PERSON] et al.** \\ **et al.** \\ \end{tabular} } & \multicolumn{2}{c}{**The Current**} \\  & & **[15]** & & **[17]** & & **Approach [7]** & & **et al. [2]** & & \multicolumn{2}{c}{**Work**} \\ \cline{2-11} \cline{3-11}  & **Acc.** & **Time** & **Acc.** & **Time** & **Acc.** & **Time** & **Acc.** & **Time** & **Acc.** & **Time** & **Acc.(\%)** & **Time** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Acc.(\%)** & **Time** \\ \hline Computer -generated -Scans of & 29.1 & 0.3 & 30.9 & 0.1 & 30.8 & 65.4 & 37.9 & 55.0 & 71.3 & 731.3 & **84.4** & **46.3** \\ actual fabrics & 11.6 & 0.2 & 16.0 & 0.1 & 33.5 & 61.0 & 40.9 & 49.0 & 54.3 & 335.8 & **65.8** & **39.4** \\ All images & 13.0 & 0.2 & 17.2 & 0.1 & 33.3 & 61.3 & 40.7 & 49.4 & 55.7 & 366.7 & **67.3** & **40.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of quantitative results: accuracy and time cost over different subsets of RPD dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Filter Selection** & **Activation Peaks Extraction (a)** & **Consistent Displacement Vector Selection (b)** & **Repeated Pattern Size Optimization (c)** & **Total** \\  & **(a)** & **(b)** & **(c)** & **(d) + (e)** & \\ \hline [PERSON] et al. [2] & 0.1 & 0.0 & 51.4 & N/A & 51.6 \\ [PERSON] et al. [12] & 0.1 & 36.1 & 327.1 & N/A & 363.2 \\ The current work & 5.9 & 0.0 & 33.9 & 0.0 & 40.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Per-step time cost (s) of all deep feature-based methods. with AlexNet. The FLOPs and memory usage were computed based on this standardized input size, providing a fair comparison of computational resources. The calculations follow the standard convolutional FLOPs formula [43], and memory usage estimations consider both model parameters and intermediate feature maps [44]. For [PERSON] et al. [2], who utilized approximately two-thirds of AlexNet's feature maps, the total FLOPs and memory usage were estimated at 0.48 GFLOPs and 40 MB, respectively. [PERSON] et al. [12], leveraging all feature maps of AlexNet, required 0.72 GFLOPs and 60 MB. In contrast, the current method, which processes only one feature map per convolutional layer in AlexNet, significantly reduced the computational burden to 0.1 GFLOPs and 10 MB while maintaining superior accuracy and efficiency. These results highlight the effectiveness of the proposed feature map selection strategy in reducing computational and memory costs. It is worth noting that traditional methods, [PERSON] and [PERSON] [17], [PERSON] et al. [15], and the SIFT-based approach [7], process images at their original resolutions without resizing. As their computational burden depends on the image size, no direct comparison of FLOPs and memory usage is provided. However, the lack of feature map selection or optimization strategies in traditional methods suggests that their computational efficiency is generally lower than that of the deep learning-based approaches.