## 3 Materials and Methods
### Methods
#### 3.4.3 Multi-Source Feature Aggregation Module  
Traditional FPN uses feature maps of different resolutions to perceive the various scales of the object. Through continuous upsampling and cross-layer feature fusion mechanisms, the output features contain both low-level visual information and high-level semantic information. However, since information only flows from high-level to low-level, the information integration of low-level features is limited and there is a lack of bottom-up information feedback. In addition, FPN treats all input features equally during the feature fusion process and fails to fully consider the important differences in features of different resolutions in the output results, making it difficult to effectively utilize the unique information of each feature map. Inspired by [19], we densely connect multiple feature maps output by the EEMA-VGG16 sub-network and propose the MSFA module in combination with LSKA. Its structure is shown in Figure 6.  
Specifically, each level of input feature map \(f_{k}\) (\(k\) = 1,2,3,4,5) first undergoes a \(1\times 1\) convolutional operation to unify the number of channels to 128, thereby reducing the computational cost of the model in the subsequent inference process and obtaining a new feature map \(f^{\prime}_{k}\) (\(k\) = 1,2,3,4,5). Next, \(f^{\prime}_{4}\) is input into the weighted feature fusion (WFF) block together with the upsampled \(f^{\prime}_{5}\) to achieve preliminary feature fusion through a weighted mechanism. In this process, the weight is determined by a normalized learnable two-dimensional vector to measure the importance of different feature maps in the fusion. It should be noted that to clearly distinguish the fusion process of two feature maps and three feature maps, we use \"WFF-2\" and \"WFF-3\" in the figure, respectively. According to the two fused features of the 4 th level on the left side of Figure 6, the calculation process can be described by the following formula:  
\[O_{WFF2}=Conv\left(\frac{w_{1}\cdot f^{\prime}_{4}+w_{2}\cdotresize(f^{\prime} _{5})}{w_{1}+w_{2}+c}\right) \tag{1}\]  
\[O_{WFF3}=Conv\left(\frac{w^{\prime}_{1}\cdot f^{\prime}_{4}+\overset{w^{\prime }_{2}\cdot f^{\prime}_{4}+w^{\prime}_{3}\cdotresize(F_{3})}}{w^{\prime}_{1}+w ^{\prime}_{2}+w^{\prime}_{3}+c}\right) \tag{2}\]  
where \(w_{i}\) and \(w^{\prime}_{i}\) are normalized weights used to measure the importance of feature maps. \(c\) is a very small value to avoid data instability. _resize_(.) is an upsampling or downsampling operation on feature maps of other levels. _Conv_(.) is a \(3\times 3\) depth-wise convolution used to eliminate aliasing effects and further feature fusion. Since the initial weighted fusion is relatively simple, the fused feature map needs to undergo more complex feature processing. To reduce unnecessary information interference and improve the expression ability of features, we further introduced LSKAB. As shown in  
Figure 6: The structure of the MSFA module (**left**) and LSKA block (**right**). Figure 6, LSKAB combines the advantages of convolution and self-attention mechanisms. It first captures local structural information through depth-wise convolution and a small receptive field convolutional kernel while enhancing the adaptability of the channel dimension; then, it uses dilated convolution with depth-wise convolution, and a large receptive field convolutional kernel simulates long-range dependence and improves the perception of global information. In addition, LSKAB decomposes the two-dimensional convolution kernel in depth-wise convolution into stacked horizontal and vertical one-dimensional convolutional kernels, significantly reducing computational complexity and memory usage while maintaining performance. After the initial fusion of \(f^{\prime}{}_{4}\) and the upsampled \(f^{\prime}{}_{5}\) is completed, the results are input into LSKAB again to enhance the receptive field and adaptively recalibrate the feature weights, thereby generating the optimized feature map \(f^{\prime\prime}{}_{4}\).