# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 3 Method
### Edge Guidance Module  
In deep learning architectures, the acquisition of high-level semantic features predominantly depends on the hierarchical stacking of convolutional and pooling operations. Nevertheless, this sequential feature extraction paradigm exhibits inherent limitations, particularly in preserving crucial image details such as edge information. This deficiency becomes especially pronounced during pixel-level classification at object boundaries, where the degradation or loss of edge information significantly compromises segmentation accuracy. To mitigate the issue of edge information attenuation in deep feature extraction, we propose an Edge-Guided Fusion (EGF) module that enhances boundary segmentation precision through explicit edge modeling and multi-scale feature augmentation strate  
Figure 3: Structure of the Dynamic Feature Fusion (DFF) module. It adaptively fuses features from two adjacent levels (F1, F2 shown as example). High-level features (F2) undergo deformable upsampling (see Equation (7)) before concatenation with low-level features (F1). A channel attention mechanism (Conv 1 \(\times\) 1 -> Sigmoid) generates dynamic fusion weights (”W”) based on the combined features to produce the fused output (F3). gies. As illustrated in Figure 4, the proposed architecture comprises three fundamental technical components:  
Edge Feature Encoding: Integrating shallow feature \(C_{1}\in\mathbb{R}^{H/4\times W/4\times 256}\) from the backbone network with deep feature \(E_{4}^{\prime}\in\mathbb{R}^{H/32\times W/32\times 256}\) from the encoder to establish a comprehensive multi-scale edge representation. \[E_{edge}=\Gamma(Conv_{3\times 3}([Up_{8}(E_{4}^{\prime})\oplus C_{1}])) \tag{10}\]  
where \(Up_{8}(\cdot)\) signifies \(8\times\) bilinear upsampling, while \(\Gamma(\cdot)\) represents the enhanced channel attention mechanism:  
\[w_{c}=\sigma(Conv_{1\times 1}(GAP(E_{edge}))) \tag{11}\]  
\[E_{edge}^{\prime}=E_{edge}\otimes w_{c}+E_{edge} \tag{12}\]  
A simple yet effective channel attention mechanism using Global Average Pooling (GAP) followed by a \(1\times 1\) convolution (Equation (11)) was chosen here for its low computational overhead while still being able to effectively highlight channels carrying significant edge information within the fused C1 and \(E_{4}^{\prime}\) features. Edge Supervised Learning: Incorporating auxiliary supervision signals to strengthen edge perception capabilities, with the ground truth edge map being generated through an enhanced Canny operator. \[\mathcal{L}_{edge}=\frac{1}{N}\sum_{i=1}^{N}BCE(Up_{4}(E_{edge}^{\prime}),G_{ edge}) \tag{13}\]  
Multi-scale Feature Injection: Constructing an edge feature pyramid to facilitate cross-level guidance. \[\{E_{edge}^{k}\}=\{Down_{2^{k-1}}(E_{edge}^{\prime})\},k=1,\dots,4 \tag{14}\]  
Multi-scale features are integrated with backbone features via a gating mechanism. \[F_{k}=Conv_{1\times 1}([E_{k}^{\prime}\oplus(E_{edge}^{k}\otimes\sigma(Conv_{3 \times 3}(E_{k}^{\prime})))]) \tag{15}\]  
While our focus is on integrating edge guidance directly within the panoptic framework via the EGF module and auxiliary loss, we note that alternative approaches exist for edge-aware segmentation. These include post-processing segmentation masks with Conditional Random Fields (CRFs) or employing multi-task learning frameworks that explicitly  
Figure 4: Structure of the Edge Guidance Fusion (EGF) component within the Edge Guidance Layer. It fuses shallow backbone features (C1, denoted L1) with deep encoder features (E4’, denoted L4 after upsampling) using convolutions. Channel attention enhances edge-relevant features (’Edge Feature’). This module facilitates auxiliary edge supervision during training. predict edge maps alongside semantic or instance segmentation. A direct quantitative comparison to these diverse methods is beyond the scope of this study, which concentrates on enhancing a unified panoptic model.