## 3 Proposed Method
Figure 6 provides a clearer visualization of the results, with the red dashed line marking the performance threshold for real-time processing, purple points representing leading segmentation techniques, and the red point denoting the DriveSegNet introduced in this study. Figure 7 presents a qualitative comparison of segmentation results under different module configurations. It is evident that the progressive incorporation of FPL, PCAM, and LAFM leads to more accurate and coherent segmentation outputs compared to the baseline. Specifically, FPL enhances boundary clarity, PCAM improves semantic consistency, and LAFM refines multi-scale feature fusion. These observations demonstrate the effectiveness of the proposed modules in enhancing both segmentation quality and visual coherence. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** \\ \hline Enet [12] & 68.4 & 76.6 & 72.2 \\ SegNet [5] & 67.8 & 74.2 & 70.8 \\ ERFNet [43] & 79.5 & 84.5 & 82.1 \\ ESNet [46] & 78.8 & 85.5 & 82.0 \\ LEDNet [47] & 80.4 & 84.6 & 82.4 \\ CGNet [50] & 72.5 & 80.7 & 76.4 \\ \hline DriveSegNet & 82.5 & 85.3 & 83.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance comparison of Recall, Precision, and F1 Score between DriveSegNet and other semantic segmentation networks on the Cityscapes dataset. Figure 6: Comparison of MAFNet with some state-of-the-art methods on the Cityscapes dataset. Figure 7: Qualitative comparison on the Cityscapes dataset. (**a**–**f**) represent the baseline model, baseline model + FPL, baseline model + FPL + PCAM, baseline model + FPL + LAFM, and baseline model + FPL + PCAM + LAFM, respectively. #### 4.4.2 Comparison on the CamVid Dataset

To further validate DriveSegNet, a comparison was conducted on the CamVid dataset against the latest methods, including LBN-AA [28], BiSeNet2 [14], TD\({}^{4}\)-Bise18 [48], SFNet [36], STDC2-Seg75 [16], S\({}^{2}\)-FPN18 [17], S\({}^{2}\)-FPN34 [17], and S\({}^{2}\)-FPN34M [17], etc. Throughout the training process on the CamVid dataset, the network maintained the input image dimensions of 720 \(\times\) 960. Table 6 clearly illustrates the performance of DriveSegNet on this dataset, attaining a 74.6% mIoU and achieving an inference rate of 139.8 FPS. Experimental results demonstrate that DriveSegNet achieves a performance improvement of 0.7% to 22.1% in mIoU compared with state-of-the-art counterparts, while maintaining comparable inference efficiency across benchmark datasets. The experimental results on the CamVid dataset comprehensively validate the effective trade-off that DriveSegNet achieves between segmentation accuracy and inference speed. These results highlight the superiority and potential of the key modules designed in this research. Table 7 compares the Recall, Precision, and F1 Score of DriveSegNet with other semantic segmentation networks on the Camvid dataset. DriveSegNet outperforms all other methods, achieving the highest values of 81.2%, 82.1%, and 81.65%, respectively. These results confirm DriveSegNet's superior performance in both segmentation accuracy and efficiency. Figure 8 provides a clearer visualization of the comparative results on the CamVid dataset, where the purple points represent the state-of-the-art segmentation methods, and the red point denotes the DriveSegNet proposed in this study.