## 6 Discussion  
This paper proposes the MEAG-YOLO model for accurately and rapidly identifying the PPE of workers in substations. The model employs specific feature extraction and optimization strategies. This enhances detection accuracy and makes the model lightweight. Consequently, it signifies substantial progress in the development of lightweight deep learning models tailored for particular application scenarios. This study delves deeply into the problem of detecting the PPE of substation workers and proposes an improved version of the YOLOv8n model. Initially, the MSCA module is integrated into the model's backbone network. This approach significantly improves the model's ability to recognize PPE in substations. Next, to meet the requirements for lightweight design, the EC2f structure was innovatively designed. This architecture streamlines feature processing while minimizing parameter count and complexity. Furthermore, the AFF module was introduced to enhance detection performance. This module strengthens feature fusion, improving accuracy and efficiency. Considering the need for efficient computation, GhostConv was combined with YOLOv8n. This method enhances computational efficiency and reduces the number of parameters by minimizing convolution operations. The GhostConv module fundamentally reduces the computational complexity  
Figure 16: Comparison of model detection results. (**a**) YOLOv8n’s detection results; (**b**) MEAG-YOLO’s detection results; (**c**) Local magnification of YOLOv8n’s detection results; (**d**) Local magnification of MEAG-YOLO’s detection results. of convolutional neural networks by modifying standard convolutional layer operations. Specifically, it splits traditional convolutions into pointwise convolutions for linear transformations and cost-effective depthwise separable convolutions for nonlinear transformations. This approach decreases the model's parameters and computational load, boosting efficiency on edge devices. However, the simplified computation may compromise feature extraction, particularly with datasets in complex weather conditions. Although standard convolutions gather extensive feature information, the simplified approach of the GhostConv module may struggle to interact fully with all input features, potentially missing critical details. This shortfall could marginally lower the model's performance under severe conditions. To address this issue, future research could consider two improvement strategies. One strategy involves using both the GhostConv module and traditional convolutions in critical parts of the model to maintain sensitivity to complex features. Additionally, introducing attention mechanisms and dynamically adjusting weight distributions can optimize feature extraction, enhancing the capture of important information. Overall, the GhostConv module significantly lowers computational demands, making it ideal for resource-limited, real-time processing environments. As shown in Table 3, MEAG-YOLO excels in detecting PPE in substation scenarios. Compared to previous studies, two-stage target detection algorithms, such as Faster R-CNN, have shown improved performance. However, they suffer from low computational efficiency, limited detection capability for small targets, a large number of parameters, and slow inference. On the other hand, single-stage algorithms like SSD can directly predict and are simpler to train, but they have lower accuracy and limited feature extraction capability in complex scenarios. Therefore, MEAG-YOLO aims to enhance detection accuracy in complex scenarios while reducing the number of parameters and complexity. However, the model proposed in this paper still has some limitations. Firstly, the model needs large quantities of accurately annotated training data. Acquiring these data is both time-consuming and costly, especially in substations with variable weather conditions.