# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 4 Experiments
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Network** & **DLK+DFF** & **Edge-Guided** & **Dual-Path** & **PQ** & **PQ\({}^{\text{th}}\)** & **PQ\({}^{\text{th}}\)** & **AP** & **IoU** \\ \hline Mask2 Former & - & - & - & 62.1 & 54.8 & 67.3 & 37.3 & 77.5 \\  & ✓ & - & - & 63.4 & 56.8 & 68.2 & 38.5 & 79.3 \\ PSM-FFEG & ✓ & ✓ & - & 64.1 & 58.6 & 68.8 & 39.6 & 79.8 \\  & ✓ & ✓ & ✓ & **64.7** & **59.4** & **69.1** & **40.7** & **80.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of set ablation experiments on the dataset Cityscapesval. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **PQ (\%)** & **FLOPs (G)** & **Parameters (M)** & **FPS** & **Inference (ms)** \\ \hline Mask2 Former & 62.1 & 74.3 & 43.8 & 36.1 & 27.7 \\ PSM-FFEG & **64.7** & 89.6 & 46.5 & 29.3 & 34.1 \\ Difference & +2.6 & +15.3 & +2.7 & –6.8 & +6.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Computational Cost and Performance between PSM-FFEG and Mask2 Former. 1. Enhanced recognition of small objects and details: Whether it is a long-distance traffic light, pedestrian, or traffic sign on a city street, or an animal, distant object, or background in the COCO dataset, PSM-FFEG shows stronger recognition and accurate segmentation capabilities. The red box area highlights the obvious omission or ambiguity of Mask2 Former on these small targets or details, while PSM-FFEG can capture and outline them more effectively. This is due to the better use of multi-scale information by the dynamic feature fusion module. 2. Improved accuracy in boundary and edge processing: Improved accuracy of boundary and edge processing: Across the two datasets, PSM-FFEG shows significant advantages in processing object edges. Whether it is the boundary between vehicles and roads/backgrounds, the outlines of buildings and the sky, or the boundaries between animals and grass, athletes, etc., the edges generated by PSM-FFEG are clearer, smoother, and closer to the real contours. In contrast, the edges of Mask2 Former sometimes appear fuzzy, jagged, or have slight penetration. This directly confirms the effectiveness of the edge guidance module (EGF) in optimizing boundary representation. While these qualitative results focus on comparing the final segmentation masks, visualizing confidence scores or uncertainty maps could offer further insights into model behavior. Generating and analyzing such maps was considered outside  
Figure 5: Qualitative comparison of panoptic segmentation results on Cityscapes. Figure 6: Qualitative comparison of panoptic segmentation results on COCO. the scope of the current comparative analysis but represents a potential direction for future work. 3. Robustness and consistency in complex scenes: In the crowded baseball scene or animal scene as shown in Figure 6, as well as the complex urban intersection (Figure 5), PSM-FFEG appears to perform better in dealing with object occlusion, dense instances, and complex backgrounds. It seems better able to distinguish adjacent instances (e.g., players in Figure 6), reducing mis-segmentation and category confusion compared to the baseline. We hypothesize that the richer features from the dynamic fusion modules and the enhanced pixel-query interaction in the dual-path decoder contribute to this improved robustness. The interactive optimization mechanism of the dual-path decoder likely plays a key role. However, we note that this assessment is primarily qualitative, as we did not employ specific datasets or metrics designed to quantitatively measure performance under varying levels of occlusion.