# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 4 Experiments
### Ablation Study  
To verify the contribution of each module of the network model in this paper, ablation experiments were conducted on the Cityscapes dataset to better analyze the PSM-FFEG model and focus on validating the effectiveness of its key components. For this purpose, the following experiments were designed, mainly focusing on four core components: Dynamic Large Kernel (DLK) for multi-scale feature extraction, Dynamic Feature Fusion (DFF) for multi-scale feature fusion, Edge-guided Fusion (EGF), and Dual Path Update Strategy. Each of these components was evaluated separately by gradually removing them. In terms of experimental settings, ResNet-50 was used as the backbone network and tested in the PSM-FFEG model. To comprehensively evaluate the performance of the model, DLK, DFF, EGF, and the Dual-Path Transformer Decoder were added to the original network for experimentation. The results are shown in Table 4, where \"-\" indicates that the component was not used in the network structure, and \"\(\check{\vee}\)\" indicates that it was used.  
By Table 4 experimental data, using the baseline network Mask2 Former algorithm as the basic experiment, the experimental results PQ is 62.1%. By gradually adding various innovative modules, it can be observed that performance gradually improves. First, after adding the Dynamic Multi-Scale Feature Extraction (DLK) and Dynamic Feature Fusion (DFF) modules, the PQ value increases from 62.1% to 63.4%, improving by 1.3 percentage points. Especially, it performs particularly well in handling object categories (\(PQ^{th}\)), increasing from 54.8% to 56.8%, which verifies the effectiveness of the feature fusion module in multi-scale feature extraction and fusion. Further introducing the Edge-guided module, the model performance continues to improve, with PQ reaching 64.1%, compared to the previous stage, improving by 0.7 percentage points. Notably, \(PQ^{th}\) significantly increases to 58.6%, indicating that the introduction of edge information is particularly helpful for improving the segmentation effect of object categories. While this internal ablation quantifies the benefit of the EGF module within our panoptic framework, a direct quantitative comparison to traditional semantic segmentation methods focused solely on edge preservation (e.g., using boundary F-measure metrics) is complex due to differing tasks and evaluation protocols. Our focus here is demonstrating improvement over a strong panoptic baseline.  
At the same time, the AP value also increases to 39.6%, indicating an improvement in overall detection performance. Finally, isolating the effect of the Dual-path strategy by comparing the model performance with it (last row, Table 4) versus without it (second-to-last row, Table 4), the model achieves the best performance: the PQ value reaches 64.7% (+0.6% gain), with \(PQ^{th}\) and \(PQ^{st}\) reaching 59.4% (+0.8%) and 69.1% (+0.3%), respectively, and IoU reaching 80.3% (+0.5%). This demonstrates that the Dual-path strategy effectively enhances feature interaction and further optimizes the segmentation effect across differ ent categories. Overall, by gradually stacking various innovative modules, the model achieves continuous improvement in all evaluation metrics. These experimental results fully prove the effectiveness of each module, which complement each other in different aspects: DLK+DFF provides strong feature extraction and fusion capabilities, the Edge-guided module enhances boundary details, while the Dual-path strategy further optimizes overall performance.