## 4 Experiments
### Experiments' Results
#### 4.4.2 Quantitative Evaluation  
A quantitative evaluation using the RPD dataset, recording the computed time cost and resulting detection accuracies of various methods, is summarized in Table 1. Again, in the table, quantitative analysis was separated into two subsets of data: computer-generated images and actual fabric scan images. The table shows that the current work achieved higher accuracy than the other SOTA methods. The overall accuracy of the proposed method was the highest (67.3% on average) among all the methods, and so was the subsets' performance: the average accuracy of actual fabric scan images was 65.8% and 84.4% for the computer-generated images. Compared to the baseline method [12], the accuracy of the current method was 20% higher and the time cost was only 11% of the baseline. Figure 9: Qualitative comparison on _computer-generated_ images (along different columns) from results of (**a**) [PERSON] and [PERSON] [17], (**b**) [PERSON] et al. [15], (**c**) [PERSON] et al. [2], (**d**) [PERSON] et al. [12], (**e**) SIFT [7], (**f**) the ground truth, and (**g**) the proposed method, respectively. [PERSON] et al. 's [12] average accuracy of actual fabric scanned images and computer-generated images were 54.3% and 71.3%, respectively, which are both higher than the corresponding results of [2]. Especially in the computer-generated category, the result of [PERSON] et al. [12] is nearly twice better than that of [PERSON] et al. [2]. It can demonstrate the influence of the number of selected filters on the accuracy of the method; the less the filter is used, the lower the accuracy. It also justifies the reason for adding an optimization process in the current method. Moreover, traditional methods [7; 15; 17] perform poorly quantitatively on both real fabric and computer-generated images. [PERSON] et al. [15] performed the worst among all the methods. Table 1 also shows the efficiency evaluation, namely the time cost, of all the methods, in addition to the accuracy performance (by Equation (12)). The current work is 40 s on average per image of the same input size, which is one-ninth of [PERSON]'s time cost, about 10 s less than [PERSON]'s, and about 20 s less than the SIFT-based approach. Although [PERSON] and [PERSON] [17] and [PERSON] et al. [15] took a shorter time, their accuracies were very low. Therefore, the above experimental results have demonstrated that the current method design achieves a good balance between accuracy and efficiency. To further analyze the efficiency of methods in Table 1, the per-step time costs of the proposed method (with respect to Figure 2) and other deep feature-based methods are compared in Table 2. Consistent displacement vector selection (step (c)) is the most time-consuming step of all the above deep feature-based methods. Displacement vector number and running time are positively correlated. In other words, the more filters that are used in a method, the more displacement vectors are created and thus the more time it will take in the voting process. As a result, the number of filters is negatively correlated to the final runtime. Consequently, by applying a filter selection algorithm, the proposed method achieved much better efficiency than that of [12]. In addition to time cost, the computational expenses of the deep learning-based methods, [PERSON] et al. [2] and [PERSON] et al. [12], and the current work, are further evaluated in terms of floating-point operations (FLOPs) and memory usage in Table 3. For these methods, all input images were resized to \(224\times 224\) pixels to ensure compatibility