# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 4 Experimental Analysis
### Experiments
#### 4.3.2 Comparative Experiments  
To verify the superiority of the proposed algorithm, we conducted a detection performance comparison among several advanced detection frameworks, namely Faster R-CNN [22] and RT-DETR [46]. Since the proposed ETS-YOLOv7 is based on the YOLO architecture, we specifically compared it with the latest version of YOLOv5s [28], YOLOv8l [47], and YOLOv9s [48]. The experimental results are shown in Table 3. All the listed detection frameworks were trained on the same UA-DETRAC dataset; the hyperparameters and experimental environment were kept consistent for a fair comparison.  
As shown in Table 3, ETS-YOLOv7 achieves higher mAP and FPS compared to other networks such as Faster R-CNN, YOLOv5s, YOLOv8l, YOLOv9s, and RT-DETR-L. The YOLO series models perform particularly well in detecting the bus category. While the two-stage algorithm Faster R-CNN excels in detecting the car category, its performance in detecting the others category lags significantly behind the YOLO series, and its FPS is lower than that of the YOLO models. RT-DETR-L, a transformer-based object detection algorithm, does not perform well in vehicle detection and has a low FPS, indicating room for significant improvement in future research. Although ETS-YOLOv7's precision for the car category is 1.6% lower than that of Faster R-CNN, it achieves the highest precision in the bus, van, and others categories, as well as the highest average precision and FPS. This balance between detection speed and accuracy makes it well-suited for large-scale traffic surveillance systems.  
The detection results on the UA-DTRAC dataset for different models are shown in Figure 6. As seen in the first and second columns, Faster R-CNN, YOLOv5s, YOLOv8l, YOLOv9s, and RT-DETR-L exhibit varying degrees of missed detections for distant small  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**[EMAIL_ADDRESS](\%)**} & \multirow{2}{*}{**Precision(\%)**} & \multirow{2}{*}{**Recall(\%)**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**GFLOPs**} \\ \cline{2-2} \cline{6-8}  & **Rainy** & & & & & & & \\ \hline YOLOv7 & 89.3 & 85.5 & 82.9 & 86.1 & 93.77 & 97.69 & 36,795,723 & 103.7 \\ \hline ETS-YOLOv7 & 91.7 & 88.9 & 86.5 & 88.2 & 94.29 & 98.93 & 31,658,443 & 80.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between YOLOv7 and ETS-YOLOv7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**[EMAIL_ADDRESS](\%)**} & \multirow{2}{*}{**[EMAIL_ADDRESS](\%)**} \\ \cline{2-2} \cline{6-8}  & **Car** & **Bus** & **Van** & **Others** & **[EMAIL_ADDRESS](\%)** & **Fps** \\ \hline Faster-rcnn & 86.9 & 88.3 & 83.8 & 79.1 & 84.5 & 50 \\ \hline YOLOv5 s(r7.0) & 81.4 & 87.5 & 82.3 & 83.6 & 83.7 & 144 \\ \hline YOLOv8 l & 83.8 & 91.2 & 85.1 & 87.7 & 86.9 & 81 \\ \hline YOLOv9 s & 83.8 & 91.5 & 85.2 & 89.1 & 87.4 & 136 \\ \hline RT-DETR-L & 74.2 & 81.1 & 73.2 & 74.6 & 75.3 & 48 \\ \hline ETS-YOLOv7 & 85.3 & 93.9 & 87.6 & 92.8 & 90.2 & 149 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different detection methods.  
objects. For the large bus target in the second column, RT-DETR-L shows a confidence score of only 0.3. In the third column, only YOLOv9s, RT-DETR-L, and ETS-YOLOv7 successfully detected the truncated vehicle target at the edge of the image, with RT-DETR-L detecting the most truncated targets but misclassifying a car as a bus. In the fourth column, despite differences in confidence scores, Faster R-CNN, YOLOv5s, and YOLOv8l all incorrectly identified the front part of a bus as a car. While missed detections of small objects remain, the algorithm effectively reduces both false alarm and missed detection rates, improves the detection of vehicle targets in dark and occluded conditions, confirming the accuracy and practicality of the algorithm.