# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 4 Experimental Results
### Comparison to State-of-the-Art Methods  
#### 4.4.1 Comparative Experiments in Cityscapes  
Quantitative Analysis: In order to further verify the effectiveness of our proposed method, we compare the proposed method with real-time and non-real-time semantic segmentation networks, in which the non-real-time segmentation networks are SegNet, FCN, DeepLabv2, SA-FFNet, RefineNet, PSPNet, AttaNet and FLANet. The real-time segmentation networks are ENet, ESPNet, ICNet, ERFNet, AGLNet, DABNet, LEDNet, DSANet, FDDWNet, BiSeNet V2, SwiftNet and LMFFNet. Experimental results in Table 3 show that our proposed algorithm achieves the highest segmentation accuracy in the lightweight network, with a segmentation accuracy of 76.9%. The segmentation results for individual categories of different models on the Cityscape test set are shown in the table, achieving state-of-the-art results in 9 out of 19 categories. Compared with the second place in the segmentation accuracy of the single class, the segmentation accuracy of the class of some slender objects is significantly improved, in which the mIoU value of the pole is increased by 4.9%, which further proves the effectiveness of the proposed algorithm in improving the accuracy of thin object segmentation. For some small objects, the proposed method also has good improvement effects. Among them, the traffic light is increased by 3.1%, the traffic sign is increased by 3.8%, the person is increased by 1.7%, and the rider is increased by 3.2%. Experiments show that the algorithm is effective for small objects. For a more comprehensive and fair comparison, we contrast the number of parameters (Params), GFLOP, inference speed (FPS), and mIoU (%) of different networks. The experimental results in Table 4 show that when using the lightweight backbone network (MobileNet V3-Small), the number of parameters of DGMNet is only 2.4 M, the mIoU reaches 76.9%, and the FPS is 141.5. Compared with other real-time segmentation networks, DGMNet has the highest accuracy, and the segmentation speed also reaches a relatively high level. Compared with LMFFNet, we achieved a better improvement in both speed and accuracy performance; compared with STDC-Seg, although the detection speed is slightly reduced, the segmentation accuracy is improved by 3.5%. Although the FPS is reduced, it can still meet the real-time requirements, and compared with the non-real-time network, the accuracy of DGMNet exceeds some networks. In order to make a fair comparison with the non-real-time network and verify the effectiveness of our proposed algorithm, ResNet101 is  
Figure 6: Ablation study results and the ground truth (GT). used as the backbone network of the network. Compared with the non-real-time network, our proposed network can still achieve the highest segmentation accuracy, and the FPS is 14.9. In conclusion, our proposed network achieves a balance between speed and accuracy and is highly competitive with other segmentation networks. Qualitative Analysis: For a qualitative analysis of the proposed network, the visualization results of DGMNet on the Cityscapes test dataset are shown in Figure 7. The experimental results in show that the proposed method has a good segmentation effect for the pole and other thinner objects and produces fine segmentation results near the bound