# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 4 Experiments
### Experimental Results and Analysis  
To validate the efficacy of the proposed approach, comprehensive experiments were conducted under identical environmental and configuration settings, comparing the PSM-FFEG model against various panoptic segmentation models utilizing Res50 as the backbone. We employed widely recognized and representative metrics in panoptic segmentation, namely PQ, \(PQ^{th}\), and \(PQ^{st}\). The experimental results on both Cityscapes val and MS COCO panoptic 2017 val datasets are presented in Table 1 and Table 2, respectively. The findings demonstrate that our proposed panoptic segmentation method, incorporating feature fusion and edge guidance, surpasses all comparative methods under the same backbone network configuration. On the Cityscapes val dataset, PSM-FFEG achieved performance metrics of 64.7%, 59.4%, and 69.1% for PQ, \(PQ^{th}\), and \(PQ^{st}\), respectively. These results represent significant improvements of 2.6, 4.6, and 1.8 percentage points over the baseline Mask2 Former model. On the MS COCO dataset, PSM-FFEG demonstrated notable performance with PQ, \(PQ^{th}\), and \(PQ^{st}\) metrics reaching 54.3%, 61.4%, and 44.3%, respectively. These results signify substantial improvements of 2.4, 3.7, and 1.3 percentage points over the baseline Mask2 Former model. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Backbone** & **PQ** & **PQ\({}^{\text{th}}\)** & **PQ\({}^{\text{st}}\)** \\ \hline RT Panoptics [17] & Res50 & 58.8 & 52.1 & 63.7 \\ UPSNet [6] & Res50 & 59.3 & 54.6 & 62.7 \\ SE-PSNet [18] & Res50 & 60.0 & 55.9 & 62.9 \\ EfficientPS [19] & Res50 & 60.3 & 55.3 & 53.9 \\ CCPSNet [20] & Res50 & 60.5 & 56.9 & 63.1 \\ PanopticFCN [21] & Res50 & 61.4 & 54.8 & 66.6 \\ Mask2 Former [9] & Res50 & 62.1 & 54.8 & 67.3 \\ OneFormer [22] & Res50 & 62.7 & 55.8 & 68.4 \\ K-Query [10] & Res50 & 63.2 & 56.2 & 68.3 \\ Ours & Res50 & **64.7** & **59.4** & **69.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of panoptic segmentation results on the Cityscapes validation dataset. To comprehensively assess the segmentation efficacy of our proposed method across diverse object categories, we conducted an in-depth analysis of 19 distinct categories within the Cityscapes dataset (as detailed in Table 3). Table 3 meticulously presents the PQ enhancements of our method relative to Mask2 Former across various categories in the Cityscapes dataset. The model demonstrates consistent improvements across all 19 categories, exhibiting robust performance across objects of varying scales. Particularly noteworthy are the substantial improvements observed for small-scale objects (including motorcycles, traffic lights, and traffic signs), with enhancements of 6.8, 4.3, and 3.4 percentage points, respectively. The ablation study results presented in Table 4 suggest that both the Dynamic Multi-Scale Feature Fusion (DLK+DFF) and the Edge Guidance (EGF) modules contribute significantly to these gains, as indicated by the step-wise improvements in \(PQ^{th}\) upon their introduction. Furthermore, the model achieves stable performance gains for medium-scale objects (such as pedestrians and riders) and large-scale objects (including trucks, fences, and buses). This comprehensive performance enhancement validates the effectiveness of our model's innovative design \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Backbone** & **PQ** & **PQ\({}^{\text{th}}\)** & **PQ\({}^{\text{st}}\)** \\ \hline UPSNet [6] & Res50 & 42.5 & 48.6 & 33.4 \\ Unifying [23] & Res50 & 43.4 & 48.6 & 35.5 \\ IDNet [24] & Res50 & 42.1 & 47.5 & 33.9 \\ CCPSNet [20] & Res50 & 43.0 & 49.2 & 33.6 \\ PanopticFCN [21] & Res50 & 44.3 & 50.0 & 35.6 \\ K-Net [25] & Res50 & 47.1 & 51.7 & 40.3 \\ Max-DeepLab [26] & Max-X & 48.4 & 53.0 & 41.5 \\ Mask2 Former [9] & Res50 & 51.9 & 57.7 & 43.0 \\ OneFormer [22] & Res50 & 52.2 & 57.9 & 43.5 \\ K-Query [10] & Res50 & 52.9 & 58.9 & 43.8 \\ Ours & Res50 & **54.3** & **61.4** & **44.3** \\ \hline \hline \end{tabular}
\end{table}