## 6 Discussion
Although the MSCA and ASFF modules enhance feature extraction and fusion, their robustness under extreme weather, variable lighting, and diverse backgrounds still needs further validation. Future research plans to broaden the dataset to encompass scenarios such as personnel wearing work attire and safety belts, and to gather data under challenging weather conditions like rain, fog, or snow. These enhancements aim to boost the model's overall performance. Additionally, future research aims to include more samples from edge positions to even out data distribution. Since each image can contain multiple targets from the same category, the number of instances per category can surpass 2800. This could result in the model performing better in these categories but worse in others. To address this issue, strategies such as oversampling, undersampling, or adjusting category weights are planned. These methods aim to balance the sample distribution and enhance the model's ability to recognize minority categories. In terms of network architecture, the MSCA module currently shows limited robustness in handling drastic target scale variations. The ASFF module may increase the computational load when processing high-dimensional features, limiting the model's deployment and application. The GhostConv module aims to reduce computational resource consumption but may be insufficient in feature extraction when dealing with complex features. Furthermore, the model design prioritizes lightweight and efficiency, yet computational resource limitations are a major consideration in practical deployments. In edge computing environments, the limited processing power and storage of processors can further affect model performance. While the GhostConv module reduces computational resource use, its ability to extract features from large, complex images may be insufficient, potentially impacting application outcomes. Despite the MEAG-YOLO model's strong test performance, future work will explore additional optimization strategies, such as enhancements to algorithms and network structures, to boost the model's generalization capabilities and practical utility. In the future, the research will incorporate imagery captured in diverse weather conditions. This will be performed by generating images with GAN networks. The study also intends to develop layered or recursive attention structures to increase the efficiency of feature extraction. A key focus will be on devising effective multi-scale feature fusion strategies, aiming to more adeptly amalgamate information across various scales. Meanwhile, the research plans to optimize grouped convolution strategies and introduce efficient activation functions to enhance feature extraction efficiency. Furthermore, this study will consider knowledge distillation strategies to reduce parameter counts and computational requirements. This approach will enhance the model's applicability with limited hardware resources. The model proposed in this study demonstrated outstanding performance in testing datasets. However, its practical value hinges not just on performance but also on scalability across different environments, ease of deployment, and integration capabilities with existing systems. Future studies will assess the model's scalability on large datasets and multiple computing platforms. This evaluation will illuminate how the model performs under increasing data loads and computing demands, providing insights for further refinements. Deployment challenges include ensuring hardware compatibility, managing software dependencies, and simplifying maintenance. It is also essential to safeguard user data privacy and security during operation. Moreover, effective integration of the model is critical. It enhances practical deployment and fosters collaboration with other systems, boosting security measures. The MEAG-YOLO model proposed in this study performed excellently on a custom dataset. It also reduced complexity. Future studies will comprehensively evaluate the model's practicality and applicability.