## 3 Proposed Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **Params (M)** & **mIoU (\%)** & **FPS** \\ \hline LBN-AA [28] & MobileNetV2 & 6.2 & 68.0 & 39.3 \\ Enet [12] & - & 0.36 & 52.8 & 60.0 \\ SegNet [5] & - & 29.5 & 59.7 & 67.7 \\ LEDNet [47] & ResNet & 0.95 & 66.6 & 61.1 \\ BiSeNet2 [14] & ResNet18 & - & 68.3 & 116.3 \\ TD\({}^{4}\)-Bise18 [48] & ResNet18 & - & 72.6 & 25.0 \\ SFNet [36] & ResNet18 & - & 73.8 & 36 \\ STDC2-Seg75 [16] & STDC2 & - & 73.9 & 97.0 \\ CGNet [50] & - & 0.5 & 65.6 & 66.1 \\ S\({}^{2}\)-FPN18 [17] & ResNet18 & 17.8 & 68.7 & 124.2 \\ S\({}^{2}\)-FPN34 [17] & ResNet18 & 27.9 & 71.0 & 107.2 \\ S\({}^{2}\)-FPN34M [17] & ResNet18 & 27.9 & 73.9 & 55.5 \\ \hline DriveSegNet & STDC1 & 11.4 & 74.6 & 139.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with state-of-the-art real-time methods on the CamVid test set. The symbol “_” indicates that the metric was not reported in the original paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** \\ \hline Enet [12] & 73.1 & 61.5 & 66.8 \\ SegNet [5] & 76.7 & 78.8 & 77.8 \\ ERFNet [43] & 80.0 & 77.3 & 78.8 \\ ESNet [46] & 79.5 & 78.4 & 78.9 \\ LEDNet [47] & 79.3 & 77.3 & 78.1 \\ CGNet [50] & 76.7 & 78.8 & 77.8 \\ \hline DriveSegNet & 81.2 & 82.1 & 81.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance comparison of Recall, Precision, and F1 Score between DriveSegNet and other semantic segmentation networks on the Camvid dataset. #### 4.4.3 Comparison on the ADE20k Dataset

On the ADE20K dataset, our DriveSegNet achieves the highest accuracy with the fastest inference speed, as demonstrated in Table 8. Specifically, DriveSegNet attains an impressive 108.4 FPS with 35.8% mIoU, surpassing BiSeNetV2-L [15] by 1.7 FPS while improving mIoU by 7.3%. Compared to the Parallel Information Interaction Network for Real-Time Semantic Segmentation, Small Version (PIDNet-S) [51], DriveSegNet not only achieves a 34.9 FPS advantage but also enhances mIoU by 1.0%. Given the diverse image content and semantic categories in ADE20K, these results robustly validate the generalization capability of our proposed SCTNet. ## 5 Conclusions

This paper presents the Dynamic Real-time Integrated Vision Encoder-Segmenter Network (DriveSegNet), a real-time semantic segmentation network based on an encoder-decoder architecture, designed to optimize the trade-off between segmentation performance and processing speed in real-time applications. The architecture incorporates a Positional Convolutional Attention Module (PCAM), which facilitates not only precise boundary

Figure 8: Comparison of MAFNet with some state-of-the-art methods on the CamVid dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mIoU (\%)** & **FPS** & **Params (M)** \\ \hline BiSeNetV2-L [15] & 28.5 & 106.7 & 3.5 \\ PIDNet-S [51] & 34.8 & 73.5 & 7.8 \\ SeaFormer-B [8] & 34.1 & 41.9 & 8.6 \\ TopFormer-B [7] & 33.4 & 94.7 & 5.1 \\ BiSeNetFormer [33] & 35.3 & 90.9 & 16.8 \\ \hline DriveSegNet & 35.8 & 108.4 & 11.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with other state-of-the-art real-time methods on ADE20K. delineation and detailed classification but also generates semantic-driven feature representations, aiding in spatial analysis and decision boundary identification. To address issues of information loss and feature mismatching during feature fusion, a Lightweight Adaptive Fusion Module (LAFM) is proposed. By introducing a weighting mechanism and a convolutional attention mechanism, LAFM dynamically adjusts feature contributions, enabling weighted fusion of multiple input features. This design enhances the model's robustness when handling complex semantic and detailed information. Extensive experiments and qualitative analysis validate the advantages of this approach in multi-scale feature fusion.