# Multiplexing Multi-Scale Features Network for Salient Target Detection
## 3 Interpretation
### Performance Analysis  
In order to verify the effectiveness of the proposed algorithm in significant target detection, the proposed algorithm is compared with MINet [21], EGNet [23], BASNet [24], BANet [25], and PiCANet [26]. These five current mainstream salient target detection networks are compared in the following table, which presents the results of this comparison. As demonstrated in Table 1, to fully compare the proposed method with existing models, detailed experimental results for the three indicators are presented. From the results, our method performs well and outperforms its competitors. This method consistently outperformed all competitors, in terms of the three indicators, on most datasets. In terms of MAE, F-measure, and SSIM, its performance improvements averaged 0.0065, 0.036, and 0.042. The test set contains a variety of different types of images, including large detection targets, small detection targets, numerous detection targets, and complex detection backgrounds. To validate the efficacy of this method, a subjective analysis of these image types is conducted. (A) The saliency map results of detecting a small target are shown in Figure 5.  
Figure 5 illustrates that the present method achieves satisfactory results in the contest of small target detection. Due to the limited number of target features, it is challenging to accurately extract the salient target's position. Furthermore, saliency information is prone to being processed as background, which hinders the precise localization of the target. The MMF-Network effectively addresses the issue of distinguishing between significant and insignificant pixels in these images by combining multi-scale features using the SimAM. This integration enhances the accuracy of target detection. (B) The image results of detecting multiple salient targets are shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**DUTS-Te**} & \multicolumn{3}{c}{**HKU-IS**} & \multicolumn{3}{c}{**PASCAL-S**} \\ \cline{2-10}  & **MAE \(\downarrow\)** & **F Price \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE \(\downarrow\)** & **F Price \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE \(\downarrow\)** & **F Price \(\uparrow\)** & **SSIM \(\uparrow\)** \\ \hline Ours & **0.039** & 0.880 & **0.803** & **0.031** & **0.886** & **0.811** & **0.066** & **0.887** & 0.798 \\ \hline MINet & 0.041 & 0.877 & 0.798 & 0.034 & 0.882 & 0.805 & 0.068 & 0.873 & 0.790 \\ EGNet & 0.049 & 0.793 & 0.797 & 0.037 & 0.874 & 0.802 & 0.078 & 0.795 & 0.562 \\ BASNet & 0.044 & 0.860 & 0.776 & 0.034 & 0.887 & 0.791 & 0.072 & 0.744 & 0.723 \\ BANet & 0.040 & **0.881** & 0.731 & 0.033 & 0.884 & 0.784 & 0.073 & 0.708 & 0.702 \\ PiCANet & 0.052 & 0.866 & 0.781 & 0.046 & 0.840 & 0.795 & 0.077 & 0.868 & **0.799** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of MMF-Network with five other models. Figure 5: Comparison of small-target detection results. Figure 6 shows that the method proposed in this paper is capable of more accurately identifying multiple significant targets in the context of multi-object detection, whereas the detection results of other detection algorithms are slightly lower than those of the MMF-Network. The discrepancy between the multi-object contour information presented and its true value may be attributed to the proximity of the objects in the image, which causes the network to correctly identify their edge details. The results of detecting a significant target image are shown in Figure 7.  
It can be seen from Figure 7 that our method is better than other methods in detecting significant targets, although the MMF-Network approach may result in missing significant targets and incomplete edges in certain instances. This is attributed to the limitations of the current salient detection algorithm, which cannot fully encompass all significant pixels. The detection results of images with complex detection scenes are shown in the figure below.