## 3 Proposed Method
Compared to DFANet-A [44], DriveSegNet-1 shows a 1.3% improvement in mIoU on the test dataset, along with a 30.5 frames per second increase in inference speed. When compared to Enet [12], DriveSegNet-1 achieves a 14.3% improvement in mIoU on the test set and a 53.6 FPS increase in speed. Although DriveSegNet-1 does not achieve the highest accuracy, its inference speed significantly surpasses that of other models. In terms of segmentation accuracy, DriveSegNet-2 exhibits a 1.9% higher mIoU than SwiftNetRN-18 on the validation set, and a 1.4% higher mIoU on the test set, along with an increase of 31.2 FPS in inference speed. When compared to TD\({}^{4}\)-Bise18 [48], DriveSegNet-2 shows a 2.0% improvement in mIoU on the test set, with a 22.9 FPS increase in inference speed. Relative to CSFNet2 [34], DriveSegNet-2 outperforms by 0.3% in mIoU on the test set, with a 1.8 FPS increase in speed. Furthermore, DriveSegNet demonstrates highly competitive performance compared to other real-time segmentation methods, achieving both high segmentation accuracy and inference speed, with excellent results on both the validation and test sets. Building on the previous metrics, we report Recall, Precision, and F1 Score for several comparison models, as summarized in Table 5, to evaluate their segmentation performance. The experimental results indicate that DriveSegNet outperforms all other models across all evaluation metrics, achieving an F1 Score of 83.8%, significantly surpassing models such as Enet [12], SegNet [5], ERFNet [43], ESNet [46], LEDNet [47], and Context-Guided Network (CGNet) [50]. These results validate the proposed model's advantage in achieving both efficient and high-precision semantic segmentation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**GPU**} & \multirow{2}{*}{**Resolution**} & \multicolumn{2}{c}{**mIoU**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**Params**} \\ \cline{3-4} \cline{6-9}  & & & & **Val** & & **Test** & \\ \hline Enet * [12] & - & Nvidia Titan X & \(512\times 1024\) & - & 58.3 & 76.9 & 0.7 M \\ SegNet * [5] & - & NVIDIA V100 & 360 \(\times\) 640 & 57.8 & 56.1 & 52.8 & 29.5 M \\ ERFNet * [43] & - & Nvidia Titan X & \(512\times 1024\) & - & 69.7 & 41.7 & 2.1 M \\ BiSeNetV1-L * [14] & ResNet18 & Nvidia Titan X & \(768\times 1536\) & 74.8 & 74.7 & 65.5 & - \\ BiSeNetV2-L * [15] & & GTX 1080 Ti & \(512\times 1024\) & 75.8 & 75.3 & 47.3 & - \\ DFANet-A * [44] & XceptionA & NVIDIA Titan X & \(1024\times 1024\) & - & 71.3 & 100.0 & 7.8 M \\ SwiftNetRN-18 * [45] & ResNet18 & GTX 1080 Ti & \(512\times 1024\) & 75.4 & 75.5 & 39.3 & 11.8 M \\ ESNet [46] & - & GTX 1080 Ti & \(512\times 1024\) & 70.4 & 70.7 & 53.1 & 1.66 M \\ LEDNet [47] & ResNet & GTX 1080 Ti & \(512\times 1024\) & 70.6 & 69.2 & 57.1 & 0.95 M \\ TD\({}^{4}\)-Bise18 [48] & ResNet18 & - & - & 75 & 74.9 & 47.6 & - \\ LBN-AA [28] & MobileNetV2 & Nvidia Titan X & \(448\times 896\) & & 73.6 & 51.0 & 6.2 M \\ HyperSeg-M [49] & EfficientNet-B1 & GTX 1080 Ti & \(512\times 1024\) & 76.2 & 75.8 & 36.9 & 10.1 M \\ HyperSeg-S [49] & EfficientNet-B1 & GTX 1080 Ti & \(768\times 1536\) & 78.2 & 78.1 & 16.1 & 10.2 M \\ CSFNet2 [34] & STDC2 & RTX 3090 & \(512\times 1024\) & - & 76.3 & 72.3 & 19.37 M \\ BiSeNetFormer * [33] & STDC1 & NVIDIA V100 & \(512\times 1024\) & - & 75.4 & 59.0 & 13.1 M \\ DCFM * [35] & MiT-B1 & Tesla V100 & \(768\times 1536\) & - & 73.1 & 72.2 & - \\ \hline DriveSegNet-1 & STDC1 & RTX 3090 & \(512\times 1024\) & 72.9 & 72.6 & 102.5 & 11.4 M \\ DriveSegNet-2 & STDC1 & RTX 3090 & \(768\times 1536\) & 77.3 & 76.9 & 70.5 & 11.4 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with state-of-the-art real-time methods on Cityscapes. Inference speed marked with * was re-evaluated on an NVIDIA RTX 3090 GPU.