## 4 Experimental Results and Analysis
### Comparison Experiment
#### 4.3.4 Comparison Experiments and Visualization of Different Models  
To evaluate the effectiveness of the proposed algorithm, this study performs cross-sectional comparison experiments on the expanded dataset using five models, namely Faster-RCNN, YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv4-tiny. The experiments use the configuration parameters listed in Table 1.  
The experimental results, as shown in Table 5, demonstrate that the algorithm proposed in this paper reduces computation and network parameter overhead, achieves a  
Figure 10: Comparison of original and added DIoU-NMS networks ((a) for the original network; (b) for added DIoU-NMS network).  
fast inference speed, and maintains high average precision. Compared with the other five models, it is more suitable for deployment in computing power-limited environments and can provide sufficient preparation for subsequent intelligent transportation systems on lower embedded terminals.  
The detection results of the improved model are visualized in experiments to demonstrate the effectiveness. The detection result graphs for the UA-DETRAC and self-built datasets are shown for five scenarios: peak period, flat peak period, low peak period, nighttime, and cloudy and rainy days, as shown in Figure 11. Compared to the original model, the improved model can effectively solve the problem of vehicle leakage and false detection in the vehicle intensive peak period, and the target box regression is closer to the vehicle target. In the night scene, the detection performance is reduced due to the influence of light. Combined with the ablation experiment of Section 4.3.3, it can be concluded that the improved model performs well in the proposed five scenarios and has apparent advantages compared to the original network.  
Figure 11: Comparison of original model results (**b,e**) and optimized model results (**c,f**) for two datasets: (**a,d**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _[EMAIL_ADDRESS] & _Frame Time_ & _FPS_ & _FLOPs_ & _Model Size_ \\ \hline Faster-RCNN & 0.9035 & 54 ms & 18.51 & 201.07 GFLOPS & 309 MB \\ YOLOv3 & 0.9341 & 29.06 ms & 34.41 & 141 GFLOPS & 124.4 MB \\ YOLOv3-tiny & 0.8443 & 15.31 ms & 65.32 & 23.64 GFLOPS & 18.9 MB \\ YOLOv4 & 0.9478 & 41.11 ms & 24.321 & 168.56 GFLOPS & 245.6 MB \\ YOLOv4-tiny & 0.8934 & 23.66 ms & 42.26 & 36.34 GFLOPS & 25.8 MB \\ Proposed & **0.987** & **14 ms** & **71.42** & **11.6 GFLOPS** & **10.8 MB** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different models.  
From Figure 11, it can be observed that in the dense traffic scenario during peak hours, the improved model can effectively detect small target vehicles in both the standard and self-built datasets at Conf = 0.25, IoU = 0.45, ensuring high detection accuracy and precision. During periods of plateau and low-demand, the improved model can detect vehicles that produce false images due to the fast vehicle speed. In rainy and night-time scenarios, detection becomes challenging due to the impact of weather and lighting conditions, resulting in varying degrees of missed detection and false alarms for both the original and improved networks. However, the improved network has an overall lower false detection rate and missed detection rate for small, distant targets and vehicle occlusion, demonstrating significant advantages over the original network.