# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 4 Experiments
### Experimental Results and Analysis

Table 2: Comparison of Panoptic Segmentation Results on the COCO Dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **Mask2 Former** & **PSM-FFEG** & **Improvement** \\ \hline Road & 98.5 & 99.1 & 0.6 \\ Sidewalk & 86.7 & 88.9 & 2.2 \\ Building & 92.3 & 94.2 & 1.9 \\ Wall & 58.4 & 63.8 & 5.4 \\ Fence & 62.1 & 65.7 & 3.6 \\ Poles & 65.3 & 68.9 & 3.6 \\ Traffic Light & 70.2 & 74.5 & 4.3 \\ Traffic Sign & 79.8 & 83.2 & 3.4 \\ Vegetation & 92.4 & 93.8 & 1.4 \\ Terrain & 62.8 & 65.9 & 3.1 \\ Sky & 94.9 & 95.7 & 0.8 \\ Person & 82.6 & 85.4 & 2.8 \\ Rider & 65.1 & 68.8 & 3.7 \\ Car & 94.3 & 95.9 & 1.6 \\ Truck & 68.7 & 74.3 & 5.6 \\ Bus & 78.9 & 83.6 & 4.7 \\ Train & 71.2 & 75.8 & 4.6 \\ Motorcycle & 65.4 & 72.2 & 6.8 \\ Bicycle & 77.2 & 80.5 & 3.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy of each category on the Cityscapes dataset. in feature extraction, multi-scale fusion, and edge guidance, demonstrating its adaptability to diverse segmentation requirements across various scenarios. To fully evaluate the practicality of the PSM-FFEG model, we perform a detailed analysis of its computational cost and compare it with the baseline Mask2 Former model. Table 5 shows the performance and computational overhead comparison of the two models on the Cityscapes dataset. As can be seen from Table 5, although the PSM-FFEG model improves the PQ index by 2.6 percentage points compared with Mask2 Former, its computational complexity also increases accordingly. Specifically, the floating point operations (FLOPs) of PSM-FFEG increased by about 20.6%, the number of parameters increased by about 6.2%, and the frame rate (FPS) decreased by about 18.8%. In practical applications, this means that the average processing time per frame of an image increased by about 6.4 milliseconds. This increase in computational cost mainly comes from three aspects:  
Dynamic multi-scale feature fusion module: Cascaded convolution and multi-dimensional attention mechanism in DLK and DFF modules contribute about 60% of the additional computational overhead Edge guidance module: EGF module and its multi-scale edge information injection account for about 20% of the additional computation Dual-path decoder: The interactive pixel-query optimization strategy increases the computational workload by about 20% compared with the standard decoder It is worth noting that although the inference speed of PSM-FFEG is reduced, it can still achieve a performance of nearly 30 FPS in actual application scenarios, which is sufficient to meet the real-time requirements of most autonomous driving and robotic vision tasks. In particular, the significant performance improvement for small objects and complex scene boundaries (as shown in Table 3, traffic lights +4.3%, motorcycles +6.8% PQ improvement) may be enough to offset this moderate increase in computational overhead in many application scenarios. To provide a more intuitive demonstration of our method's segmentation capabilities, we randomly selected three complex environments from the Cityscapes and COCO panoptic segmentation datasets for panoptic segmentation visualization. The results are compared with those generated by the Mask2 Former method, as illustrated in Figures 4 and 5 (displaying, from left to right: the input image, ground truth annotation, Mask2 Former output, and our method's output). Critical areas are highlighted with red bounding boxes. By comprehensively observing the segmentation results in Figure 5 (Cityscapes) and Figure 6, it can be clearly seen that the proposed PSM-FFEG method outperforms the Mask2 Former baseline model in several key aspects: