## 3 Method
### Improved MEAG-YOLO Model Structure
#### 3.2.4 GhostConv Module  
The precise and prompt detection of PPE in substations is crucial. While existing deep learning models have improved detection accuracy, they often lack lightweight and real-time capabilities. The YOLOv8n model consists of numerous convolutions and has a high parameter count and complex computations. To streamline the model, this study incorporates the GhostConv module into the YOLOv8n framework. This integration aims to simplify computations and enhance model efficiency. By adopting the GhostConv module, this research focuses on reducing complexity while maintaining performance. This can meet the requirements of high-detecting efficiency substation scenarios. The GhostConv module uses grouped convolution technology instead of traditional convolution. This significantly reduces the model's parameter count and computational complexity. The method maintains performance while reducing computational resource consumption. This reduction is crucial for real-time processing environments with limited resources. The module breaks down standard convolutions into lighter operations, which reduces redundant computations in convolutional layers. This reduction eases the computational load. The design simplifies the network structure and reduces energy consumption. It also enhances computational efficiency, making the model more suitable for edge devices. There may be a slight performance drop, but this is acceptable in substation environments where real-time responsiveness is crucial. Overall, the introduction of the GhostConv module increases processing speed and decreases resource use. This enables the YOLOv8n model to meet the needs of modern monitoring systems that require fast and efficient computation while maintaining detection accuracy. The structure of GhostConv is shown in Figure 8.  
The GhostConv convolution process occurs in three steps. Firstly, the input image generates an original feature map through standard convolution.  
\[Y^{\prime}=X*f^{\prime} \tag{5}\]  
In Equation (5), the input data are \(XeR^{c\times h\times w}\), where \(c\) represents the number of input channels, and \(h\) and \(w\) are the height and width, respectively. \(Y^{\prime}\) represents the Ghost output feature map, \(*\) denotes the convolution operation, and \(f^{\prime}\) is the size of the convolution kernel. Next, the original feature map is transformed into multiple Ghost feature maps through a linear transformation.  
\[y_{ij}=\Phi_{i,j}(y^{\prime}_{i}) \tag{6}\]  
Next, the original and Ghost feature maps merge to create the final output feature map, with \(\Phi_{i,j}\) indicating the feature mapping and \(y_{ij}\) being the output, as shown in Equation (6).  
This study utilized the GhostConv module to optimize the convolution layers in both the backbone and neck networks of the YOLOv8n model. The implementation of lightweight linear operations significantly decreases the parameter count and enhances the speed of inference. While this optimization slightly reduces detection accuracy, the resulting lightweight model is better suited for real-time detection in resource-constrained environments.