# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 3 Method
### Dynamic Multi-Scale Feature Fusion Module
#### 3.2.1 Dynamic Large Kernel Module  
As illustrated in Figure 2, the DLK module facilitates multi-scale feature extraction through a combination of deformable kernel cascading and attention selection mechanisms. The processing pipeline comprises three distinct stages:  
Cascaded Deformable Convolution: A dual-path architecture is implemented utilizing depthwise separable convolution, specifically configured as:  
\[\begin{cases}\text{Path1}:5\times 5\text{ Convolution, dilation 1}\\ \text{Path2}:7\times 7\text{ Convolution, dilation 3}\end{cases} \tag{1}\]  
This specific configuration of cascaded 5 \(\times\) 5 (dilation 1) and 7 \(\times\) 7 (dilation 3) kernels was chosen empirically. It effectively achieves a large 23 \(\times\) 23 receptive field (calculated via Equation (2)) necessary for capturing broad context, while using depthwise separable convolutions and controlled dilation helps manage computational cost compared to a single, very large dense kernel.  
Leveraging the receptive field accumulation effect, an equivalent 23 \(\times\) 23 large kernel operation is achieved. The computational formulation is expressed as follows:  
\[R_{i}=R_{i-1}+(k_{i}-1)\times j_{i} \tag{2}\]  
Where \(R_{i-1}\) denotes the effective receptive field of the (i\(-\)1)th layer, \(k_{i}\) represents the kernel size of the ith layer, and \(j_{i}\) corresponds to the stride or dilation rate of the ith layer. Given that the first layer employs a 5 \(\times\) 5 convolution kernel with dilation = 1, the initial \(R_{0}\) = 1, consequently yielding \(R_{1}\) = 1 + (5\(-\)1)\(\times\)1 = 5. The subsequent layer utilizes a 7 \(\times\) 7 convolution kernel with dilation=3, resulting in an effective kernel size of 7 \(\times\) 3 = 21 (due to dilation = 3), and \(R_{2}\) = 5 + (19\(-\)1)\(\times\)1 = 23. Through the cascade of these two convolutional layers, an effective receptive field size analogous to 23 \(\times\) 23 is achieved. This cascading approach effectively circumvents the substantial computational burden associated with the direct implementation of larger convolution kernels.  
Dual-dimensional dynamic selection: A novel channel-spatial cooperative attention mechanism is introduced, with its computational process described as follows:  
\[A_{s}=\sigma(Conv_{7\times 7}([MaxPool(F);AvgPool(F)])) \tag{3}\]  
\[A_{c}=\sigma(MLP(GAP(F))+MLP(GMP(F))) \tag{4}\]  
\[F^{\prime}=(F\otimes A_{s})\odot A_{c} \tag{5}\]  
where \(A_{s}\) and \(A_{c}\) correspond to the spatial and channel attention maps, respectively, while \(\otimes\) and \(\odot\) signify element-wise multiplication and channel-wise multiplication operations.  
This dual-dimensional attention mechanism allows the DLK module to adaptively modulate input features based on channel importance and spatial relevance for the given context. The term 'dynamic' here refers to this adaptive feature weighting learned through attention, rather than a process of explicitly selecting or discarding entire feature channels or spatial locations.  
Residual Learning: Incorporating cross-layer connections to facilitate enhanced gradient propagation.  
\[F_{out}=F_{in}+Conv_{1\times 1}(F^{\prime}) \tag{6}\]