# Research on Vehicle Target Detection Method Based on Improved YOLOv8
## 4 Experiments
### KITTI 2D
A comparison of the PR curves of the original models YOLOv8n and YOLO-AL on the KITTI 2D dataset is shown in Figure 7. A larger area under the curve indicates superior model performance, as it signifies the model's ability to achieve higher recall while maintaining high precision. Therefore, PR curve plots offer a comprehensive performance comparison and facilitate a more accurate evaluation of the model's effectiveness in multiscale vehicle target detection tasks. After integrating the ADown and LSCD modules, the model's [EMAIL_ADDRESS] improved from 0.934 to 0.945. This advancement significantly outperforms the base model, especially in distinguishing common vehicles such as cars and nonmotorized vehicles such as cyclists in traffic settings. The decrease in mAP for Vans and Trams could be attributed to their visual similarities. Factors contributing to this decline include the reduced discriminative ability of features due to the lightweight design of the model, loss of target specifics through downsampling, and scale and semantic misalignment caused by shared convolutions. These elements collectively influence the precision of object localization and classification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]\%** & \(\Delta\)/\%** & **Params/M** & **FLOPs/G** & **FPS** \\ \hline YOLOv8n & 93.4 \(\pm\) 0.2 & - & 3.0 & 8.1 & 259.8 \\ YOLOv8n + ADown & 93.8 \(\pm\) 0.1 & 0.4 & 2.6 & 7.4 & 265.6 \\ YOLOv8n + LSCD & 94.1 \(\pm\) 0.1 & 0.7 & 2.4 & 6.5 & 276.3 \\ YOLO-AL & 94.5 \(\pm\) 0.2 & 1.1 & 2.0 & 5.8 & 290.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment of YOLO-AL on KITTI 2D dataset. Figure 7: (a) YOLOv8n. (b) YOLO-AL. PR curve comparison of YOLO-AL on KITTI 2D dataset. Figure 8 shows a comparison of the confusion matrices of the YOLOv8n model and the YOLO-AL model. Specifically, on the KITTI 2D dataset, the YOLO-AL model reduces the leakage rate by 7%, 4%, and 1% in the categories of Car, Truck, and Bicycle, respectively, compared to the YOLOv8n model. The reason why the misclassification of Tram is reduced by 1% is that there are cases where the Tram is considered as a background. The shared parameters and the unified scale processing mechanism may not accurately distinguish between Vans and Trams with similar appearances, resulting in a 1% decrease in their respective positive classifications. Figure 9 shows the comparison of the F1 scores. A higher F1 score means that the model performs more balanced and efficiently in the detection task, capturing the majority of positive samples and reducing false positives to achieve a good balance between precision and comprehensiveness. The original model, the YOLOv8n model, has an F1 score of 0.89 in all categories. The YOLO-AL model has an F1 score of 0.92 in all categories. This shows that the YOLO-AL model outperforms the original model and can achieve better performance in detection tasks. The results of the heatmap comparison between the original YOLOv8 model and the YOLO-AL model are shown in Figure 10. A heatmap is a visualization tool that is used to analyze how the model focuses on different regions of the input image, showing the  
Figure 8: (**a**) YOLOv8n. (**b**) YOLO-AL. Confusion matrix comparison of YOLO-AL on KITTI 2D dataset. Figure 9: (**a**) YOLOv8n. (**b**) YOLO-AL. Comparison of F1 scores of YOLO-AL on KITTI 2D dataset. intensity of the network response across those areas. In a heatmap, the red regions generally indicate a higher response intensity, suggesting that the model prioritizes features in those areas for the final prediction. Lighter regions indicate less attention from the model, which implies that these areas have a lesser impact on the output. In contrast, the heat map of the YOLOv8n model shows that the attention area is relatively scattered, which indicates that it may be disturbed by more background noise during the target positioning process, resulting in a slight decrease in detection accuracy.