## 3 Experiments
### Comparative Experiments  
Comparative Experiments are carried out for the comprehensive evaluation. Among the existing methods in spatial domain, Wang [5], Jang [38], Tang [7] are traditional image segmentation methods; HOG [13], FHOG [29], GLCM [15], and LBP [14] are texture detection methods; RC [20], SF [21], BMS [22] are saliency detection methods; SSD300 [17], the You Only Look Once method (YOLOv3) [19], R-FCN [16], RefineDet321 [18] are popular object detection methods based on CNN. Due to the particularity of the research, only SR [23], FT [24], and MSS [25] are the existing saliency detection methods in the frequency domain. The experiments have the same configuration as below:  
1. [label=(0)]
2. Otsu Method [8] for binarizations;
3. cell size 16x16, block size 48x48 and classifier SVM for texture detections;
4. backbone VGG16 and pre-training weight VGG16-VOC2007 [35] for CNNs. Table 3 shows the comparison of the results of existing methods. In Table 3, not only is the final result of our method (i.e., row 20) listed, but so is the result of spatial-based texture detection and the result of frequency-based saliency detection (i.e., row 8 and row 19, respectively). Unless otherwise specified, the result of our method refers to the final result. As shown in Table 3, recalls of traditional image segmentation methods (i.e., rows 1-3) are lower than all the others because of interference factors. Texture detection methods (i.e., rows 4-8) have little time consumption, but their recalls are relatively low. It can be shown that our texture detection method (i.e., row 8) performs better than other texture-based methods, indicating \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **Time (s)** & **P (\%)** & **R (\%)** \\ \hline \(48\times 48\) & 0.415 & 94.29 & 94.29 \\ \(96\times 96\) & 1.701 & 91.2 & 92.7 \\ \(150\times 150\) & 9.12 & 88.1 & 83.8 \\ \(192\times 192\) & 10.68 & 92.5 & 88.5 \\ \(350\times 350\) & 18.75 & 84.7 & 79.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the results for different sizes of the initial saliency map. Figure 8: Comparison of results of saliency detection for different weights. effective hand-crafted texture feature. Frequency-based saliency detection methods (i.e., rows 16-19) are faster than the spatial-based ones (i.e., rows 13-15), and our saliency detection method (i.e., row 19) has the highest precision and recall rates among them. Although the popular CNN-based methods (i.e., rows 9-12) are close to ours in terms of Recall, our final method has higher precision with less time consumption. We calculated the size information of the objects detected by our final method. Statistical results show that in our testing set, when the accuracy is IoU \(>=\) 0.8, the largest object detected is 56 \(\times\) 70 pixels and the smallest object is 27 \(\times\) 31 pixels. To compensate for the inadequacy of the comparison of the above Precision and Recall (P-R) results, F1-Measures and time consumptions of all methods are intuitively shown in Figure 9.  
As shown in Figure 9, the F\({}_{1}\)-Measure of our final method (at around 0.95) is close to those of CNN-based methods, i.e., SSD300, YOLOv3, R-FCN, and RefineDet321. Meanwhile, the F\({}_{1}\)-Measure of our final method is higher than those of traditional image segmentation methods, saliency detection methods, and other texture detection methods. In addition, time consumption of our method (at