## 4 Experimental Design and Result Analysis
### Experimental Results and Analysis  
#### 4.3.1 Experimental Comparison and Analysis of Different Algorithms  
To confirm the advantages of the algorithm studied within this article, experimental comparisons were conducted with currently popular object detection models. Precision, recall, [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, parameter count, and model size were used as evaluation metrics. The algorithm studied in this paper was relative to the original models of Faster-CNN, YOLOv3-tiny, YOLOv5n, YOLOv5s, YOLOv7-tiny, and YOLOv8n under the same experimental environment. The experimental findings are detailed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Epochs & 200 \\ Batch & 32 \\ Imgsz & 640 \\ workers & 4 \\ optimizer & SGD \\ weight\_decay & 0.0005 \\ momentum & 0.937 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \(P\) **(\%)** & **R (\%)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **Params** & **GFLOPs** & **FPS** \\  & & **(\%)** & **(\%)** & **(\%)** & **(10\({}^{6}\))** & **(G)** & **(frame-s\({}^{-1}\))** \\ \hline Faster-RCNN & 50.2 & 89.8 & 82.1 & 46.5 & 28.2 & 940.9 & 11.6 \\ YOLOv3-tiny & 91.7 & 85.0 & 91.7 & 57.3 & 8.6 & 12.9 & **212.7** \\ YOLOv5n & 90.4 & 88.9 & 93.4 & 62.1 & 1.7 & **4.1** & 105.2 \\ YOLOv5s & **92.8** & 90.9 & 94.9 & 64.2 & 7.0 & 15.8 & 75.3 \\ YOLOv7-tiny & 88.5 & 90.1 & 94.3 & 62.4 & 6.0 & 13.2 & 127.5 \\ YOLOv8n & 89.7 & 89.3 & 93.5 & 64.3 & 3.0 & 8.1 & 130.2 \\ PRE-YOLO & 91.0 & **91.0** & **95.7** & **66.9** & **2.0** & 11.7 & 81.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results compared with other models.  
According to the experimental results, the precision rate \(P\), recall rate \(R\), [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 have increased by 1.3, 1.7, 2.2, and 2.6 percentage points, respectively, compared to the original YOLOv8n model, and the number of model parameters has reduced by 33.3 percentage points. Although the precision rate \(P\) has slightly decreased compared to YOLOv5s, the other indicators show significantly better performance. Additionally, the modified model has a larger parameter size, which is unfavorable for subsequent model deployment. Although the FPS of the improved model has decreased, according to the \"Technical Specifications for Video Evidence Collection Equipment for Road Traffic Safety Violations\", the standard for detection frame rate is greater than or equal to 25 frames per second. Even when pursuing higher image quality detection at 60 frames per second, this research paper retains the ability to maintain a detection rate of 81.6 frames per second, which is more than sufficient for executing detection tasks. Moreover, high-frame-rate image quality significantly occupies hardware storage space, and hardware detection platforms on traffic roads regularly have trouble with limited space and low computing power. Therefore, the optimized algorithm model put forward in this work is proficient in executing detection tasks to a higher standard.