# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 4 Experiments
### The Performance in the Testing Set Data
#### 4.2.5 Speed and Performance Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Application** & **Image Size** &
\begin{tabular}{c} **Experimental** \\ **Configuration** \\ \end{tabular} & **Speed (FPS)** \\ \hline Ours & Salient semantic segmentation & 416 \(\times\) 416 & NVIDIAGTX 1650 & 8.2 \\ YOLO V3 [25] & Object recognition & 320 \(\times\) 320 & M40 or Titan X GPU & 45.5 \\ Salient object detection [24] & Salient object detection & 400 \(\times\) 300 & NVIDIA RTX-2080 Ti GPU & 57 \\ FCN-8s [5; 26] & Semantic segmentation & 224 \(\times\) 224 & NVIDIA Titan & 15 \\ SegNet [8; 26] & Semantic segmentation & 224 \(\times\) 224 & NVIDIA Titan & 17 \\ PSPNet [6; 26] & Semantic segmentation & 224 \(\times\) 224 & NVIDIA Titan & 5 \\ DeepLab-v2 [7; 26] & Semantic segmentation & 224 \(\times\) 224 & NVIDIA Titan & 6 \\ \hline \hline \end{tabular} We only compared the real-time performance with other related work, mainly related to the input image size and GPU. We did not build high-configuration experimental conditions but simulated ordinary experimental conditions of the onboard processor with limited computing resources. The processing speed of the GTX1650 used is almost 0.58 times that of GTX TITAN X, 0.85 times that of GTX TITAN, and 0.33 times that of RTX 2080 Ti.  
\end{table} 
Table 7: Average Speed comparison of the related model architectures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Global Accuracy** & \
\begin{tabular}{c} **Class Average** \\ **Accuracy** \\ \end{tabular} & **mIOU** & **Defects** \\ \hline Ours & 62.77 & 48.42 & 37.24 & Not obvious \\ FCN-8s [5] & 40.42 & 28.71 & 25.12 & Inherent Network architecture \\ SegNet [8] & 44.76 & 35.21 & 28.13 & bottleneck and the model’s \\ PSPNet [6] & 44.19 & 36.37 & 29.53 & training require a large number of \\ DeepLab-v2 [7] & 43.35 & 32.78 & 27.26 & datasets with ground truth. \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Global accuracy** & \
\begin{tabular}{c} **Class Average** \\ **Accuracy** \\ \end{tabular} & **mIOU** & **Defects** \\ \hline Ours & 62.77 & 48.42 & 37.24 & Not obvious \\ FCN-8s [5] & 40.42 & 28.71 & 25.12 & Inherent Network architecture \\ SegNet [8] & 44.76 & 35.21 & 28.13 & bottleneck and the model’s \\ PSPNet [6] & 44.19 & 36.37 & 29.53 & training require a large number of \\ DeepLab-v2 [7] & 43.35 & 32.78 & 27.26 & datasets with ground truth. \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Global accuracy** & \
\begin{tabular}{c} **Class Average** \\ **Accuracy** \\ \end{tabular} & **mIOU** & **Defects** \\ \hline Ours & 62.77 & 48.42 & 37.24 & Not obvious \\ FCN-8s [5] & 40.42 & 28.71 & 25.12 & Inherent Network architecture \\ SegNet [8] & 44.76 & 35.21 & 28.13 & bottleneck and the model’s \\ PSPNet [6] & 44.19 & 36.37 & 29.53 & training require a large number of \\ DeepLab-v2 [7] & 43.35 & 32.78 & 27.26 & datasets with ground truth. \\ \hline \hline \end{tabular}
\begin{tabular}{c} Global accuracy (G) denotes the percentage of pixels correctly classified. Class average accuracy (C) denotes the mean of the predictive accuracy of overall classes. mIoU denotes mean intersection over union over all classes. All experiments were performed under the same experimental configuration, as shown in Table 3.  
\end{table} 
Table 8: Three commonly used performance measures. realize the model's training and has better real-time speed and performance. Our method avoids the bottleneck challenge of semantic segmentation and provides a new way to label mobile robot semantic information.