## 2 Related Work
### C3 Glost Module  
GhostNet is a new lightweight deep neural network architecture proposed by Huawei Noah's Ark Laboratory [25]. In general, a large number of redundant feature maps generated by convolution have little complementary effect on the main feature maps in the actual detection task, which is not helpful for the network to improve detection accuracy. However, generating these redundant feature maps consumes a lot of computing power. Therefore, GhostNet constructs the ghost module and uses it to generate redundant feature maps faster and more efficiently. The GhostNet lightweight network can greatly reduce the amount of calculation and parameters of the network while maintaining the size and channel size of the original convolution output feature map. The implementation principle is to divide the traditional convolution into two steps, which are ordinary convolution and cheap linear calculation. Firstly, a part of the feature map is generated by using fewer convolution kernels, then the channel convolution is performed on this part of the feature map to generate more feature maps, and finally, the two sets of feature maps are spliced to generate the GhostNet feature map. The traditional convolution and GhostNet convolution processes are shown in Figure 4.  
The head part of YOLOv5-Lite adopts multiple C3 structures, which have a large number of parameters and a slow detection speed. Therefore, this study replaces the new C3 Ghost module with the C3 module to achieve a lightweight effect. The specific structure is shown in Figure 5.  
The GhostBottleneck module is an innovative network structure component, and its design inspiration comes from the ghost module. The module is mainly composed of two GhostConv modules and a residual block. Among them, the first GhostConv module acts as an extension layer, and its core function is to increase the number of channels of the input feature map. This step is crucial because it provides a richer feature representation for subsequent deep feature extraction and information fusion. The second GhostConv module undertakes the task of dimensionality reduction, which aims to reduce the number of channels of the output feature map. This not only helps to reduce the computational complexity but also ensures that the output feature map matches the other structures in the network (such as the diameter structure) in the number of channels so as to achieve more efficient information transmission and processing. Between the two GhostConv modules, a residual edge with deep convolution processing is also embedded. This design enables the features to be effectively fused with the features of the residual edge after expansion and dimensionality reduction, thereby enhancing the expression ability of the model. In  
Figure 4: Traditional convolution and GhostNet convolution processes.  
Figure 5: Ghost module.  
addition, the main purpose of introducing depthwise convolution (DWConv) is to further reduce the number of parameters of the model, thereby reducing the computational burden and improving the practicability of the model. The C3 Ghost module is an improvement based on the C3 block. It replaces the traditional residual component Resunit with a reusable GhostBottleneck module. This replacement not only reduces a large number of convolution operations in the traditional structure but also significantly compresses the size of the model and reduces the computational complexity of the model. In this way, the C3 Ghost module achieves a lightweight model while maintaining its performance, making it more suitable for deployment and operation in resource-constrained environments. This design not only improves the efficiency of the model but also enhances its adaptability and flexibility in practical applications.