## 3 Method
### Environmental Perception Attention (EPA)
In actual models, the number of channels \(\copyright\) is often an integer power of 2, so the mapping relationship between it and k can be defined as  
\[C=\psi(k)=2^{k} \tag{3}\]  
That is,  
\[k=\psi(C)=\left|\log_{2}C\right|_{odd} \tag{4}\]  
where \(\left|n\right|_{odd}\) represents the odd number closest to \(n\). Obviously, the larger the number of input feature map channels, the larger the \(k\) value is, and the proportional relationship is still preserved. Based on the above process, the input processing steps can be expressed as  
\[X^{\prime\prime}=Sigmoid\left(Conv2D\left(Concat\left(GAP\left(X^{\prime} \right),GMP\left(X^{\prime}\right)\right)*X^{\prime}\right.\right. \tag{5}\]  
Specifically, we first unified the resolution of the large (\(F_{L}\)) and small (\(F_{S}\)) feature maps from the backbone network to the medium-scale feature map size (\(F_{M}\)), while keeping the number of channels unchanged. For small-scale feature maps, we used the nearest neighbor interpolation method for up-sampling. The nearest neighbor interpolation algorithm is not only simple and efficient, but also only considers the nearest pixel value and does not smooth the original pixel value, so it can retain the original features and edge information of the image. For the large-scale feature map \(F_{L}\), we did not use the traditional pooling or strided convolution down-sampling method, but split it to down-sample the feature map without losing learnable information [51]. The Split operation is shown in Figure 3 below. The Split operation divides the large feature map into blocks with a scale of 2, concatenates the feature values at the same relative position in each block together with their channel dimensions, and finally forms a new sub-feature map. Relative to the original input feature map \(X\in\mathbb{R}^{H\times W\times C}\), the shape of each sub-map is \(X\in\mathbb{R}^{\frac{H}{2}\times\frac{W}{2}\times C}\), which can be regarded as the result of pooling calculations using specific position values to retain the original image, which retains all information in the channel dimension and avoids information loss. Next, as shown in Figure 2, we concatenated all feature maps with the same scale to obtain the feature map \(F_{Concat}\) and stacked them in the new dimension. Then, we extracted the combination of features at different levels and scales through 3D convolution (Conv3D) to obtain a richer multi-level feature representation [52]. However, features of different scales may contain different degrees of noise, especially in low-resolution features, where noise may be more obvious. When these features are fused, the noise  
Figure 3: Split operation steps. Figure 2: Three-scale fusion module, where Split is the process shown in Figure 3.  
may be amplified, affecting the performance of the model. When dealing with complex backgrounds or low-quality images, the impact of noise may cause the model to learn incorrect features. In addition, multi-scale features may contain similar information, and this redundant information may mask important features and affect the performance of the model. Therefore, we used 3D maximum pooling (MaxPool3D) to reduce redundant information, suppress the impact of noise, and emphasize important features to enhance the robustness of the model when processing noisy data. Finally, the output features were added to the neck network (\(F_{Neck}\)), and point convolution (PConv) was used to fuse information between different channels to enhance the expressiveness of the model.