## 5 Results and Discussion
### Effect of Model Optimization  
We conducted various strategies to improve the real-time performance and accuracy of our intelligent detection system. Firstly, we performed a series of optimization operations on the dataset. Building upon that, we further optimized the training process and experiment results for the YOLOv5s model. Subsequently, we embedded the trained model into Jetson Xavier NX with the expectation of achieving promising results in real-world power construction scenarios. As the experiment involved detecting eight target categories, the model optimization stage focused on optimizing the dataset trained on the YOLO model. We employed image augmentation techniques to enhance performance. We performed data augmentation in this experiment to modify certain features of the images using techniques such as translation, zoom, rotation, and ten other augmentation operations. To optimize the dataset effectively, we randomly selected three operations to enhance the dataset during each image transformation process. The training accuracy of the model was evaluated before and after data augmentation, and the results are presented in Table 4. As shown in the table, the precision, recall, and mAP values were improved after data augmentation. These enhancements further enhanced the detection accuracy of the model in power-related scenarios. Moreover, to enhance the detection accuracy of the YOLOv5s model, we made advancements by integrating the SE module into the bottleneck structure. This integration, denoted as \"YOLOv5s+SE\", has proven effective in improving the overall performance of the model. To optimize the speed and light weight of our model, we made selective sacrifices to the precision performance of the \"YOLOv5s+SE\" model. Specifically, during the connection phase between the Neck and Prediction components, we meticulously devised suitable detection heads that take into account the size and distribution of the detection targets. This Edge-YOLO aims to enhance detection speed while minimizing any potential decrease in accuracy. Notably, we eliminated the largest header in the head structure to further accelerate detection and meet the requirements of edge devices. The YOLOv5s+SE model originally possessed 7,032,725 parameters, resulting in a training model speed of 2.7 ms. However, by incorporating SE and optimizing the detection heads, we managed to reduce the parameter count of Edge-YOLO to 5,241,454 and achieve a smaller training model size of 2.2 ms. The details are shown in Table 5. In addition, the table also showed the precision, recall, mAP, FPS, parameters, and the number of floating-point operations (FLOPs). Based on the performance comparison and analysis of the aforementioned models, Edge-YOLO achieves a balanced compromise between the \"YOLOv5s+SE\" and \"YOLOv5s-header\" models in terms of the number of FLOPs and parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & **P** & **R** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline Source & 0.897 & 0.904 & 0.931 & 0.583 \\ Augmentation & 0.958 & 0.959 & 0.980 & 0.709 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training results of the model before and after data augmentation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Items** & **Value** \\ \hline Epoch & 300 \\ Batch size & 8 \\ Worker & 8 \\ Momentum & 0.95 \\ Initial learning rate & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters setting of detection method.