## 2 Network Model
### Ghost Structure  
[PERSON] et al. [20] suggested that obtaining the feature map through convolution results in many extraneous features. To address this, they designed the Ghost model to achieve efficient de-redundancy and obtain the same number of features as regular convolution with fewer parameters. In this study, we employ the Ghostnet structure to reduce the computational and parametric requirements of the model, making it lighter and improving its inference speed.  
The GhostNet structure is analogous to the residual structure and is composed of two Ghost models. The first module performs a widening operation to increase the number of channels in the input features, followed by a ReLU activation function. The second module decreases the number of channels in the output features, enabling them to be consistently normalized and matched. This operation enables the model to preserve the detection accuracy of the original model while reducing the number of parameters and the computational effort.  
The Ghost model differs from conventional convolution in that it splits the convolution into two stages. In the first stage, a small amount of convolution calculation generates a portion of the redundant feature map. In the second stage, a separate chunked linear convolution operation is performed on a small amount of the feature map to generate the Ghost corresponding to this part of the feature map. Then, the two parts are stitched together to achieve the result of linear regulation convolution. The module structure is depicted in Figure 2.  
Assuming the input features are \(\mathrm{C}\times\mathrm{H}\times\mathrm{W}\) and the output features are \(\mathrm{C}^{\prime}\times\mathrm{H}^{\prime}\times\mathrm{N}\), with \(\mathrm{C}\), \(\mathrm{H}\), \(\mathrm{C}^{\prime}\), and \(\mathrm{H}^{\prime}\) denoting the length and width of the input and output feature maps, and \(\mathrm{W}\) and \(\mathrm{N}\) indicating the number of channels of the input and output feature maps, respectively. Using traditional convolution with a kernel size of \(\mathrm{k}\times\mathrm{k}\) (excluding the bias amount \(\mathrm{b}\)), the final computational volume is \(\mathrm{W}\times\mathrm{K}\times\mathrm{K}\times\mathrm{C}^{\prime}\times \mathrm{H}^{\prime}\times\mathrm{N}\). The Ghost Module, however, divides the computational volume into two parts: the first part generates \(\mathrm{M}\) feature maps (\(\mathrm{M}\) < \(\mathrm{N}\)) through traditional convolution, with a computational volume of \(\mathrm{M}\times\mathrm{K}\times\mathrm{K}\times\mathrm{C}^{\prime}\times \mathrm{H}^{\prime}\times\mathrm{C}\); the second part uses linear transformations to obtain the final output feature maps by passing \(\mathrm{S}\) linear operations \(\mathrm{\varphi}\). The number of output feature maps obtained via both methods should be the same, \(\mathrm{W}=\mathrm{M}\times\mathrm{S}\), and the computational volume is \(\mathrm{(S}-1)\times\mathrm{M}\times\mathrm{C}^{\prime}\times\mathrm{H}^{ \prime}\times\mathrm{A}\times\mathrm{A}\) (where one of the linear operations is a constant mapping that does not participate in the linear transformation, and the linear transformation uses  
Figure 1: The structure of the YOLOv5.  
A \(\times\) A convolution). The ratio of traditional convolution and Ghost Module computation (when K = A) is S \(\times\) N/(N + S - 1), and since S is much smaller than N, the Ghost Module reduces a significant amount of computation while maintaining the original non-redundant feature information via a simple linear transformation.