## 2 Materials and Methods
### Model Architecture
#### 2.3.3 Multiscale Graph Convolution Network (MSGCN) Module  
The CNN described above effectively captures local and global information. To create long-term dependencies and to identify the spatial relationships between different semantic details, the encoded information is sent to a large optimised topological GCN that extracts detailed semantic information and constructs spatial relationships by merging and transmitting node information [59]. A topological graph can be expressed as G = (V,E), where V is the set of nodes and E is the set of edges between nodes. The steps used to construct the spatial relationship are as follows.  
Step 1. To improve feature representation, we first send the encoded features to a \(1\times 1\) convolutional layer for information conversion. In other words, we embed the data into a unified low-dimension space to compress the features and improve the \"expressive\" nature of the network; the reconstructed features provide a basis for the subsequent graphical representation. The reconstruction process is described as follows:  
\[\left\{\begin{array}{l}O_{N}=\textit{reshape}(Conv_{1\times 1\times 2^{n}+1 \textit{w}}(O_{mp}))\\ O_{M}=\textit{reshape}(Conv_{1\times 1\times 2^{n}+1\textit{w}}(O_{mp}))\\ O_{N}\in R^{\text{C}\times(H\times W)},O_{M}\in R^{(H\times W)\times C}\end{array}\right. \tag{2}\]  
where \(O_{M}\) and \(O_{N}\) are the reconstruction features, \(O_{mp}\) is the maximum pooling layer of the convolutional encoding layer, and reshape(.) is the information conversion function.  
Step 2. The reconstructed features are used to construct a multiscale graph with dimensions of C\({}_{N}\times\) C\({}_{M}\), where C\({}_{N}\) is a channel feature with \(N\) nodes and C\({}_{M}\) is a channel feature with \(M\) nodes. The Euclidean distance is used to determine the spatial relationships between nodes. The construction of adjacency matrix \(A\) is shown in Figure 3 and described by Equation (3). The reconstructed feature map is used to construct the topology map G. If there is a relationship between adjacent feature points, a value of 1 is assigned; if not, the value assigned is 0. Next, graph convolution is used for node learning and optimisation, and node transfer is then employed to gather detailed information on spatial relationships. The multilayer learning graph convolution is given by the following equation:  
\[f_{G}^{(l)}=\sigma(\tilde{D}^{-\frac{1}{2}}\widetilde{A}\tilde{D}^{-\frac{1}{ 2}}f_{G}^{(l-1)}W_{G}^{(l-1)})\in R^{\text{C}\times\text{C}},\widetilde{A}=A+I \tag{3}\]  
where \(f_{G}^{(l)}\) is the output of the \(l\)th graph convolution layer, \(\sigma(\cdot)\) is the LeakyRelu activation function, \(\widetilde{A}\) is the adjacency matrix normalised by the Laplace method, \(\widetilde{D}\) is the degree matrix, \(I\) is the identity matrix, \(A\) is the adjacent scale, and \(W_{G}^{(l-1)}\) is the weight matrix of the \((l-1)\)th graph convolution layer.  
Step 3. The MSGCM creates effective long-term dependencies between nodes using the node transfer function and efficiently captures the global spatial semantics of target objects using the aggregation function. Given that the initial encoding features contribute to the representation of local semantics, we can reconstruct the features from the third encoding module, fuse them with the multiscale graph convolution features, and input these into the 3 \(\times\) 3 convolution layer to further strengthen the representations of local and global features and establish interactions between them.