# EGSDK-Net: Edge-Guided Stepwise Dual Kernel Update Network for Panoptic Segmentation
## 5 Conclusions  
Panoptic segmentation is a critical task in the field of computer vision, with widespread applications in areas such as autonomous driving, robotics, and medical imaging. However, despite its significance, panoptic segmentation faces challenges such as the limitations imposed by traditional segmentation frameworks and the suboptimal utilization of information in existing kernel update strategies. This paper presents a novel panoptic segmentation network, EGSDK-Net. It primarily addresses two issues: the constraints of panoptic segmentation within inherent frameworks and the low information utilization of existing kernel update strategies. To address the first issue, we take into account the relationship between edge detection and the panoptic segmentation task and propose a real-time edge guidance module. This module supervises the extracted edge features, which receive additional attention to their spatial positions, using the edges predicted by Canny as ground truth. For the other issue, we introduce a stepwise dual kernel update module. This module facilitates the rational use of information from the earlier stage by performing additional updates and combinations of the kernels from the previous stage to the next. Numerous comparison experiments and ablation studies confirm the validity and effectiveness of the proposed model. In the future, using more advanced edge detection algorithms instead of the existing Canny to generate edge ground truth appears to be a promising direction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(PQ\) & \(PQ_{th}\) & \(PQ_{st}\) \\ \hline Baseline w/RTEGM & 59.7 & 49.4 & 67.1 \\ Using Addition for Fusion & 59.5 & 50.0 & 66.7 \\ Using Multiplication for Fusion & 59.7 & 50.3 & 66.5 \\ Ours & 60.6 & 51.9 & 66.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the ablation experiments on the internal structure of SDKUM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(PQ\) & \(PQ_{th}\) & \(PQ_{st}\) \\ \hline Convs & 59.3 & 48.9 & 66.7 \\ only MaxPool & 59.6 & 50.0 & 66.7 \\ only AvgPool & 59.4 & 49.3 & 66.7 \\ Ours & 60.6 & 51.9 & 66.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the ablation experiments on the internal structure of RTEGM.  
**Author Contributions:** Conceptualization, P.M. and H.Z.; methodology, P.M.; software, P.M.; validation, P.M., H.Z. and K.M.; formal analysis, H.Z.; investigation, P.M.; resources, H.Z.; data curation, P.M.; writing--original draft preparation, P.M.; writing--review and editing, P.M.; visualization, P.M.; supervision, H.Z. and K.M.; project administration, P.M. and H.Z.; funding acquisition, K.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Provincial Science and Technology Innovation Special Fund Project of Jilin Province, grant number 20190302026 GX, Natural Science Foundation of Jilin Province, grant number 20200201037 JC, and the Fundamental Research Funds for the Central Universities for JLU.  
**Data Availability Statement:** The cityscapes dataset in this paper is openly available at [[https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/)]([https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/))(accessed on 1 March 2024).  
**Acknowledgments:** The authors are grateful to the anonymous reviewers for their insightful comments, which have certainly improved this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.