## 5 Results and Discussion
### Performance of Different Detection Models  
After optimizing the entire dataset, the same validation dataset was used to compare the detection performance of seven models in terms of both speed and accuracy. The objective of this paper was to find a YOLO model that is more suitable for embedding in edge devices and better aligned with the requirements of actual power construction site inspection detection. The training precision trend of the seven models during the first 200 epochs is presented in Figure 6, where Edge-YOLO maintained relatively stable accuracy across 200 rounds of training and demonstrated an improvement in accuracy when compared to the YOLOv5s model. Furthermore, Table 6 demonstrates that the YOLOv5n and YOLOv7-tiny models have a simpler structure and faster detection speed compared to the other models. According to the performance comparison and analysis of the models in the table, the number of FLOPs and parameters of YOLOv5n and YOLOv7-tiny are less than YOLOv5s, which will save some memory space. However, their detection precision and recall are poor, with YOLOv7-tiny in particular showing the worst accuracy. The YOLOv7x, YOLOv7, and YOLOv5m models, on the other hand, have more complex structures with a large number of FLOPs and parameters, demonstrating superior detection accuracy. Nevertheless, their detection speed is slower, making them unsuitable for embedding in edge devices that require real-time performance and greater portability. Compared to both YOLOv7 and YOLOv5 series models, the YOLOv5s model offers faster detection speeds without compromising on Precision, Recall, mAP@.5, and mAP@.5:95 metrics. Figure 7 shows the detection results of these models in the test dataset, indicating that the YOLOv5s model performs comparably to other more complex YOLO series models, but with significantly better detection accuracy than the lighter YOLOv5n and YOLOv7-tiny models. After carefully evaluating the accuracy performance close to YOLOv5m and the speed performance close to YOLOv7-tiny on edge devices, we have chosen the YOLOv5s model as our detection model to enhance compliance with real-time detection systems for on-site power construction workers. To achieve this, we proposed a novel object detection model called Edge-YOLO, which is built upon the YOLOv5s object detection algorithm and offers improvements in both detection speed and accuracy. In Figure 7, we additionally showcase the detection performance of the safety gloves to illustrate the aforementioned results. The utilization of red boxes in the figure serves to differentiate between the various types of detectors. Based on the results of safety-gloves detections, it can be observed that the YOLOv5n and YOLOv7-tiny models exhibit lower accuracy, increased false positives, and missed detections. In comparison, the detection performance of Edge-YOLO is superior to them.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & **P** & **R** & **F\({}_{\mathbf{I}}\)** & **mAP@.5** & **mAP@.5-95** & **Speed** (ms)** & **FPS** & **FLOPs** \\ \hline YOLOv5s & 0.958 & 0.959 & 0.958 & 0.980 & 0.709 & 2.5 & 32.50 & 14.6 G \\ YOLOv5s+SE & 0.969 & 0.972 & 0.968 & 0.989 & 0.729 & 2.7 & 30.09 & 17.2 G \\ YOLOv5s-Header & 0.954 & 0.955 & 0.954 & 0.980 & 0.690 & 1.9 & 36.66 & 13.1 G \\ Edge-YOLO & 0.964 & 0.966 & 0.965 & 0.983 & 0.718 & 2.2 & 35.36 & 14.2 G \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative experiments on improving model accuracy and speed.