## 3 Methods
### Global Spatial and Channel Reconstruction Convolution (GSCConv)  
In object detection networks, the neck network plays a crucial role in extracting pertinent features from the backbone, processing this data, and forwarding it to the recognition head. To enhance both accuracy and efficiency in handling complex images without increasing computational demands, this paper introduces a novel convolution module named global spatial and channel reconstruction convolution (GSCConv), which is based on the SCConv module [36].  
Drawing inspiration from the GSConv module [37], the GSCConv module incorporates the Conv, SCConv, Concat, and shuffle modules. The mathematical formulation is provided in Equation (15), as follows:  
\[X_{out}=f_{shuffle}(f_{concat}(f_{SCConv}(f_{Conv}(X_{in})),f_{Conv}(X_{in}))) \tag{15}\]  
As shown in Figure 6, in GSCConv, data undergo a flow from standard convolution to SCConv, followed by concatenation with standard convolution output. The shuffle module then processes the concatenated data, ensuring a random mixture that evenly blends information from standard convolutions into the SCConv output, promoting the exchange of local feature details across channels.  
The SCConv module within the GSCConv framework, as presented in this article, is composed of two integral units: the spatial reconstruction unit (SRU) and the channel reconstruction unit (CRU), which are depicted in Figure 7.  
SRU focuses on addressing spatial redundancy through separation and reconstruction operations.  
Initially, trainable parameters are computed to derive the information weight \(\mathbf{W_{1}}\) and non-information weight \(\mathbf{W_{2}}\). Multiplying the input feature \(\mathbf{X}\) by \(\mathbf{W_{1}}\) and \(\mathbf{W_{2}}\) yields two weighted features: \(\mathbf{X_{1}}\), with higher information content and \(\mathbf{X_{2}}\), with lower information content. Subsequently, through the cross-reconstruction operation, these distinct weighted information features are merged to produce a feature with enhanced information content, as depicted in Equation (16), as follows:  
\[\left\{\begin{aligned} & X_{1}=X\bigotimes W_{1},\\ & X_{2}=X\bigotimes W_{2},\\ & X^{w}=(X_{11}\oplus X_{22})\bigcup(X_{21}\bigoplus X_{12}). \end{aligned}\right. \tag{16}\]  
The spatial fine feature mapping \(\mathbf{X^{w}}\) retains redundancy in the channel dimension, which is a challenge addressed by CRU.  
CRU divides the input spatially refined features into two parts, \(X_{\text{up}}\) and \(X_{\text{low}}\), based on channels \(\propto\)C and (1- \(\alpha\))C. Then, it performs a transformation operation using the segmented features and performs weighted summation on the two parts to obtain the final output. The calculation process is shown in Equation (17), as follows, in which \(\beta_{1}\) and \(\beta_{2}\), respectively, represent the weight vectors of two features:  
\[X_{output}=\beta_{1}\cdot\big{(}GWC\big{(}X_{up}\big{)}+PWC\big{(}X_{up} \big{)}\big{)}+\beta_{2}\cdot(PWC\big{(}X_{low}\big{)}+X_{low}) \tag{17}\]  
Integrating SCConv into the model enhances performance by reducing redundant features, leading to decreased complexity and computational costs while maintaining performance. However, spatial information transmission to channels during image conversion may lead to some loss of semantic information due to spatial dimension compression and channel dimension expansion.  
The GSCConv module, by merging standard Conv and SCConv, combines their advantages. By integrating it into the neck, feature redundancy is minimized with SCConv while preserving inter-channel correlations through regular convolution. This integration enhances detection efficiency without increasing computational complexity, thereby meeting real-time human detection requirements effectively.  
Figure 6: Network architecture of global spatial and channel reconstruction convolution (GSCConv).  
Figure 7: Network architecture of spatial and channel reconstruction convolution (SCConv).