## 5 Results and Discussion
### Results in Realistic Power Construction Sites  
In this section, we integrated seven YOLO models into the edge device Jetson Xavier NX and evaluated their processing rates in various real-world scenarios to compare the detection performance of each model. The frame-per-second (FPS) results are presented in Table 7. Our experiment indicates that the Edge-YOLO model achieved an FPS of 35.36 on the Jetson Xavier NX, demonstrating the real-time performance of our system.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & **P** & **R** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **Speed** & **Parameter** & **FLOPs** \\ \hline YOLOv5s & 0.958 & 0.959 & 0.980 & 0.709 & 2.5 & 13.7 & 14.6 G \\ YOLOv5m & 0.978 & 0.984 & 0.991 & 0.812 & 4.2 & 40.2 & 45.3 G \\ YOLOv5n & 0.911 & 0.897 & 0.937 & 0.587 & 1.6 & 3.64 & 4.5 G \\ YOLOv7 & 0.968 & 0.972 & 0.987 & 0.765 & 7.9 & 71.3 & 96.6 G \\ YOLOv7X & 0.973 & 0.980 & 0.991 & 0.813 & 12.8 & 135.0 & 202.7 G \\ YOLOv7-tiny & 0.869 & 0.860 & 0.903 & 0.564 & 2.1 & 11.7 & 12.1 G \\ Edge-YOLO & 0.964 & 0.966 & 0.983 & 0.718 & 2.2 & 12.0 & 14.2 G \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of different detection models.  
Figure 6: Contrast of the training precision trend.  
Based on the performance comparison of various models, it can be observed that the Edge-YOLO model achieves faster detection speeds than the YOLOv5s model while maintaining satisfactory accuracy. Therefore, by embedding the Edge-YOLO model on the portable Jetson Xavier NX, we can detect the compliance of workers' protective gear more effectively in real power construction sites. To assess the robustness of our system, we conducted inspections at three power construction sites with different scenarios, demonstrating the effectiveness of our intelligent edge system. The on-site inspection dataset  
Figure 7: The detection performance of different models. The orange bounding box denotes the operators with a safety helmet, the light orange bounding box denotes the operators without a safety helmet, the red bounding box denotes the operators with a safety belt, the pink bounding box denotes the operators without a safety belt, the brown bounding box denotes the operators with work clothes, the light green bounding box denotes the operators without work clothes, the green bounding box denotes the operators with safety gloves, the cyan bounding box denotes the operators without safety gloves. The red boxes are employed to differentiate among the various types of detectors.  
includes a diverse set of inspection objects. The three scenarios were: (1) high altitude operations, (2) night operations, and (3) snowy climate operations. Figure 8 shows the deployment platform of our edge device system, while Table 8 and Figure 9 present the detection results of the test dataset. Our results demonstrate the successful detection of all eight classes in various realistic power construction scenarios, showcasing excellent detection performance across different categories and operation scenarios. It can be seen that the Edge-YOLO-based model embedded into the Jetson Xavier NX for on-site detection of compliant wearable devices among power construction workers can maintain good detection accuracy. Moreover, it offers real-time performance, making it more convenient and practical for real-world scenarios.  
Figure 9: The detection performance of different scenarios. The orange bounding box denotes the operators with a safety helmet, the light orange bounding box denotes the operators without a safety helmet, the red bounding box denotes the operators with a safety belt, the pink bounding box denotes the operators without a safety belt, the brown bounding box denotes the operators with work clothes, the light green bounding box denotes the operators without work clothes, the green bounding box denotes the operators with safety gloves, the cyan bounding box denotes the operators without safety gloves.