## 3 Methods
### Generalized Separated and Enhancement Aggregation Network (GSEAM)  
The generalized separated and enhancement aggregation network (GSEAM) is introduced to improve feature focus in low-light scenarios. Initially, feature maps at different scales are split and later recombined after Rep-NCSP module processing. Depth-wise separable convolutions with residual connections are utilized to address illumination discrepancies. A two-layer fully connected network is then used to project features into a reduced-dimensional space for better detection. GSEAM replaces the C2f module by merging different-scale feature maps in the neck network to enhance feature extraction efficiency. The RepNCSP module enhances human body feature identification by extracting spatial and semantic information through convolutional structures and residual connections. Its three layers manage feature extraction, channel fusion, and transformation, capturing intricate image details and improving performance in low-light conditions. Illustrated in Figure 4, RepConvN conducts feature extraction across scales through iterative convolutions. RepNBottleneck combines multi-layer convolutions with residuals to merge information from various levels, allowing the model to address global and local features simultaneously. Depth-wise separable convolution enhances the processing of feature maps under varying illumination by decreasing the parameter count and enhancing feature extraction efficiency [41]. To combat potential feature degradation and ambiguity stemming from uneven lighting, this paper introduces depth-separable convolution with residual connec  
Figure 4: Network architecture of RepNCSP and RepNBottleneck. (**a**) The network architecture of RepNCSP; (**b**) The network architecture of RepNBottleneck. tions within the GSEAM module, combining depth-wise and pointwise convolutions. The computational formulation for this process is outlined in Equations (7)-(9), as follows:  
\[Conv_{(i,j)}^{p}=\sum_{m}^{M}W_{P}\cdot y_{(i,j,m)} \tag{7}\]  
\[Conv_{(ij)}^{D}=\sum_{k,l}^{K,L}W_{D}\odot y_{(i+k,j+l)} \tag{8}\]  
\[DSConv_{(i,j)}=\sum_{m}^{M}W_{P}\cdot\left(\sum_{k,l}^{K,L}W_{D}\odot y_{(i+k,j+l)}\right) \tag{9}\]  
Among them, \(\mathbf{k}\), \(\mathbf{l}\), and \(\mathbf{m}\) represent offsets in the depth, width, and height dimensions, respectively, and \(y_{(i,j)}\) denotes the pixel position index on the feature map, with W as the trainable weight matrix. In this process, the input features undergo depth-wise separable convolution followed by GELU activation and BatchNorm operations. The resulting output is then added to the initial input, allowing the network to learn a residual function rather than a complete input-to-output mapping. This approach eases the learning burden by focusing on the discrepancy between the input and the desired output. Additionally, unlike the SiLU function in YOLOv8, the GELU activation function, which is detailed in Equations (10) and (11) as follows, is utilized in this part:  
\[GELU(x)=x\cdot P(X\leq x)=x\int_{-\infty}^{x}\frac{e^{-\frac{(X-x)^{2}}{2\
u^ {2}}}}{\sqrt{2\pi}\sigma}dX \tag{10}\]  
\[GELU(x)=0.5x\left[1+tanh\left(\sqrt{\frac{2}{\pi}}\left(x+0.047715x^{3}\right) \right)\right] \tag{11}\]  
Equation (10) involves the cumulative function of the Gaussian normal distribution, which is denoted as P(X \(\leq\) x), where \(\mu\) and \(\sigma\) represent the mean and standard deviation of the distribution. Since Equation (10) cannot be directly calculated, the calculation formula for the GELU activation function can also be approximated as Equation (11). After average pooling and weighting of the output results, the network architecture of the entire GSEAM module is shown in Figure 5.  
Figure 5: Network architecture of generalized separated and enhancement aggregation network (GSEAM).