# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 4 Experimental Results
### Ablation Experiment  
To verify the effectiveness of the proposed module, we design the following ablation experiments. Only the CF-path is used as the baseline; modules are added to the baseline model to verify the performance of the proposed algorithm, and the test results of each proposed module on the Cityscapes test dataset are shown in Table 1.  
Baseline: The context path uses MobileNet V3-Small to extract image features and fuse multi-scale features for image segmentation. The mIoU of the baseline is 56.3%. Baseline + FAM: The experimental results in Table 1 show that the mIoU of the network is improved from 56.3% to 61.1% after using the FAM module. It indicates that our proposed FAM module pays attention to both spatial and channel information and effectively combines multi-dimensional attention to enrich the attention map, so as to refine the features and effectively improve its segmentation performance. Baseline + FAM + DIG-Path: The mIoU of the network is improved from 61.1% to 66.7% after adding the detailed information supplementary path. The experimental results show that the DFSM module can introduce richer detail information into the deep feature \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Method**} & \multirow{2}{*}{**mIoU (\%)**} \\ \cline{2-2} \cline{4-5}  & **FAM** & **DIG-Path** & **SIS-Path** & **TPFF** \\ \hline model\_1 (baseline) & & & & 56.3 \\ model\_2 & ✓ & & & 61.1 \\ model\_3 & ✓ & ✓ & & 66.7 \\ model\_4 & ✓ & & ✓ & 67.9 \\ model\_5 & ✓ & ✓ & ✓ & 73.2 \\ model\_6 (DGMNet) & ✓ & ✓ & ✓ & 76.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation study results of the DGMNet. map, the ability of the neural network to understand and segment object details is greatly improved, thus verifying the effectiveness of this module. Baseline + FAM + SIS-Path: With the introduction of the DIG-path, the mIoU of the network is improved from 61.1% to 67.9%. It can be seen that the semantic graph path can supplement the high-level semantic information and add a connection between distant features, thereby expanding the receptive field of features, increasing the global semantic information. From another perspective, the understanding of the network on the image is increased, so as to effectively improve the segmentation performance of the network. Baseline + FAM + DIG-Path + SIS-Path: Experimental results show that using both the DIG-path and SIS-path can further improve the segmentation accuracy of the network, and the mIoU value is 73.2%. We believe that the addition of DIG-path + SIS-path can further enhance the network's learning of some small and thin objects and detail information and still retain enough detail information in the deep network. Baseline + FAM + DIG-Path + SIS-Path+ TPFF: With the addition of the feature fusion module, the performance of the network is further improved, and the mIoU value reaches 76.9%. The fused feature maps contain semantic information under different angles and make full use of the features of different paths to further improve the segmentation accuracy of the model. In the following, we discuss the proposed attention mechanism. The TPFF module fuses the feature information of different paths to obtain richer semantic information and supplement the lost detail information to a certain extent. To evaluate the effectiveness of the TPFF module, we perform the following experiments on different fusion schemes, and the experimental results are shown in Table 2. Directly using the traditional Add or Concat methods gives poor segmentation accuracy, with mIoU values of 71.5% and 73.2%, respectively. After adding an attention mechanism on the basis of Add and Concat methods, the segmentation accuracy is improved by 2.8% and 2.4%, respectively. Experimental results show that adding a full attention mechanism can effectively promote the semantic segmentation of images. Compared with the Add + Attention scheme, the proposed fusion module improves the segmentation accuracy by 2.6%.