## 4 Result
### Comparative Experiment of YOLO Series Algorithms  
In order to further verify the effectiveness of the improved algorithm, this paper sets up a comparison experiment between different YOLO algorithms. It mainly includes YOLOv3, YOLOv4, and YOLOv5. The experimental results are shown in Table 9.  
From the table, it can be seen that although YOLOv3 and YOLOv4 have improved their recognition accuracy compared to the other algorithms listed in the previous section, their detection speeds are still far from adequate. YOLOv3-tiny and YOLOv4-tiny used CSPdarknet53_tiny as the backbone network and improved on the basis of the original network. Although the detection speed has been greatly improved, the detection accuracy has dropped off a cliff. After the improved algorithm in this paper, compared with the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, the four different models have different degrees of improvement in detection accuracy and speed. The improved algorithm reduces the parameters and volume of the model. In practical applications, the appropriate detection model can be selected according to the embedded deployment of ADAS.  
From Figure 9, the improved algorithm has a higher confidence in the recognition of various types of lane lines. Especially in various complex situations, it can effectively avoid leakage or misdetection. This suggests that the improved YOLOv5 algorithm has a wider range of applications and is more suitable for ADAS applications and deployments.  
As can be seen in Figure 10, the improved algorithm improves the detection accuracy for all categories of targets to different degrees, especially for the lane line category with more complex features. When using YOLOv5s, the detection of the yellow dash-solid line is poor. However, under our improved algorithm, the detection results of the two types of short yellow dash-solid lines are improved by 11% and 11.9%, respectively, which indicates that our improved strategy has some effect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **FPS (frame\(\cdot\)s\({}^{-1}\))** & **Volume (MB)** \\ \hline Faster R-CNN(Vgg16) & 88.1 & 13.5 & 522.1 \\ Faster R-CNN(ResNet50) & 86.0 & 6.3 & 108.5 \\ \hline SSD(Vgg16) & 86.7 & 45.5 & 95.1 \\ SSD(ResNet50) & 82.8 & 68.9 & 49.8 \\ SSD(MobileNetv2) & 73.1 & 71.2 & 18.8 \\ \hline RetinaNet & 85.6 & 53.3 & 139.9 \\ \hline YOLOv5s & 92.1 & 91.3 & 13.8 \\ \hline Oure-YOLOv5s & 95.1 & 95.2 & 11.7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of results from different algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **Parameters (M)** & **FPS (frame\(\cdot\)s\({}^{-1}\))** & **Volume (MB)** \\ \hline YOLOv3 & 88.5 & 61.6 & 26.5 & 117.9 \\ YOLOv3-tiny & 76.8 & 8.7 & 140.8 & 16.7 \\ \hline YOLOv4 & 89.5 & 64.4 & 24.0 & 224.6 \\ YOLOv4-tiny & 81.5 & 5.9 & 128.4 & 22.6 \\ \hline YOLOv5s & 92.1 & 7.1 & 91.3 & 13.8 \\ YOLOv5m & 94.4 & 20.9 & 41.2 & 40.3 \\ YOLOv5l & 95.5 & 46.2 & 25.1 & 88.6 \\ YOLOv5x & 96.7 & 86.3 & 13.4 & 165.2 \\ \hline Ours-YOLOv5s & 95.1 & 6.0 & 95.2 & 11.7 \\ Ours-YOLOv5m & 95.8 & 15.8 & 43.7 & 30.6 \\ Ours-YOLOv5l & 96.1 & 32.3 & 26.9 & 62.1 \\ Ours-YOLOv5x & 96.9 & 57.0 & 15.3 & 109.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of results of different YOLO versions.  
Figure 9: Results of different YOLO algorithms: (**a**) YOLOv5s; (**b**) Ours-YOLOv5s; (**c**) YOLOv5x; (**d**) Ours-YOLOv5x; (**e**)YOLOv4-tiny.