# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 5 Conclusions  
This research introduces the PSM-FFEG panoptic segmentation framework, presenting three significant innovations to address the challenges of multi-scale feature fusion and boundary segmentation in complex scenarios: (1) A dynamic multi-scale feature fusion architecture that facilitates adaptive contextual information capture through deformable convolution cascading and dual-dimensional channel-spatial attention mechanisms. (2) An edge-guided fusion module that substantially enhances object boundary segmentation accuracy, particularly for small objects, by explicitly extracting and augmenting edge features while integrating them with multi-scale representations. (3) A dual-path decoder that elevates overall segmentation performance through an alternating pixel-query optimization strategy. Despite the promising results demonstrated on Cityscapes and MS COCO, our proposed PSM-FFEG approach has several limitations that warrant further investigation and offer avenues for future research:  
* Computational Complexity: The introduction of the cascaded convolutions and attention mechanisms within the DLK modules, the cross-scale fusion in DFF, and particularly the iterative nature of the Dual-Path Transformer Decoder contribute to increased computational complexity (FLOPs and parameters) compared to simpler baseline models like Mask2 Former with a standard decoder. This might pose challenges for deployment in real-time applications or on resource-constrained hardware without further optimization or model compression. * Hyperparameter Sensitivity: The performance of PSM-FFEG can be sensitive to the setting of certain hyperparameters. This includes the loss weights balancing the main panoptic loss terms (\(\lambda_{c}\)_e_, \(\lambda_{d}\)_ice_, \(\lambda_{c}\)_ls_) and the auxiliary edge loss (\(\lambda_{c}\)_dge_), as well as potential internal parameters within the attention or fusion modules. Achieving optimal performance currently requires careful empirical tuning for each dataset, which can be time-consuming. * Long-range Dependency Modeling: While the DLK module increases the effective receptive field and attention mechanisms capture context, the model's ability to explicitly model very long-range spatial dependencies across the entire image might still be limited compared to architectures employing global self-attention across all pixels, which are often computationally prohibitive at high resolutions. The attention mechanisms used are primarily focused within local windows or feature channels. * Potential Failure Cases: While demonstrating overall improvement, the model might still struggle in certain scenarios. These could include: objects belonging to extremely rare categories not well represented in the training data; images with severe weather, lighting, or blur exceeding training conditions; objects with highly atypical appearances; or instances where the auxiliary edge supervision was noisy or inaccurate, potentially providing misleading guidance. A systematic analysis of such failure modes was not conducted but is important for understanding robustness. These limitations motivate future research directions. We plan to explore: (1) Techniques for model compression and efficiency optimization to reduce the computational overhead of the dynamic modules and decoder; (2) Developing adaptive or meta-learningapproaches to automate hyperparameter tuning (especially loss weights); and (3) Investigating the integration of lightweight global context mechanisms or graph neural networks to enhance long-range dependency modeling without excessive cost. Main writer and literature review, L.Y. ; supervision, ideas and knowledge contribution, and review, S.T. and S.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Natural Science Foundation of Hunan Province (No. 2023 JJ30185) and the Scientific Research Fund of Hunan Provincial Education Department (No. 22A0640), and 166 Engineering Project (232 CXCYT-105010501). Not applicable.