# Research on Vehicle Target Detection Method Based on Improved YOLOv8
## 4 Experiments
### Ua-Detrac  
UA-DETRAC [39] is an open-source dataset widely used for target detection and multi-target tracking, mainly for vehicle detection and tracking tasks in traffic scenes. The UA-DETRAC benchmark dataset consists of 100 long-time traffic surveillance videos, 60 of which are used for training and 40 for testing. There are four conditions in the video: sunny, cloudy, rainy, and night. Since the UA-DETRAC dataset contains multiple similar videos, the vehicle changes and background differences between adjacent video frames are small. If used directly for training the target detection model, it may lead to data redundancy. To improve the model's generalization capability and shorten training time, this study extracts one frame every 10 frames, resulting in approximately 13,824 original video images. This method not only reduces data redundancy, but also effectively reduces the overfitting phenomenon caused by repeated learning of the same features. On this basis, 7500 images with large traffic volume and complex background changes were selected as the experimental dataset. Figure 11 shows an example of the UA-DETRAC dataset. The UA-DETRAC dataset is divided into 8:1:1 ratios, including 6000 training sets, 750 validation sets, and 750 test sets. The dataset includes labels for four vehicle categories: Car, Bus, Van, and Others, However, the Others category was excluded from this experiment. Table 5 shows the specific detection results. The lightweight vehicle detection model YOLO-AL achieved the highest [EMAIL_ADDRESS] on the UA-DETRAC dataset, reaching 92.0%. Although FLOPs are slightly larger than EfficientDet-D0 and the FPS is slightly lower than Mobilenetv4-YOLO, it has obvious advantages in other aspects. Compared to the original  
Figure 10: (**a**) Original. (**b**) YOLOv8n. (**c**) YOLO-AL. Heatmap comparison of YOLO-AL on KITTI 2D dataset. model YOLOv8n, YOLO-AL [EMAIL_ADDRESS] increased by 2.0%, the number of parameters decreased by 33%, the FLOPS decreased by 28%, and the FPS increased by 11%. Table 6 shows the comparison data from the ablation experiment. The addition of each module improves the model detection accuracy and speed while effectively reducing the number of parameters and FLOPs. The comparison of the PR curves of the original model YOLOv8n and YOLO-AL on the UA-DETRAC datasets is shown in Figure 12. Compared with the original model, the [EMAIL_ADDRESS] of the YOLO-AL model increased from 90.0% to 92.0%, with significant improvements in all categories. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]\%** & **\(\Delta\)/\%** & **Params/M** & **FLOPs/G** & **FPS** \\ \hline YOLOv8n & 90.0 \(\pm\) 0.2 & - & 3.0 & 8.1 & 252.0 \\ YOLOv8n + ADown & 90.7 \(\pm\) 0.3 & 0.7 & 2.6 & 7.4 & 267.2 \\ YOLOv8n + LSCD & 90.5 \(\pm\) 0.2 & 0.5 & 2.4 & 6.5 & 278.1 \\ YOLO-AL & 92.0 \(\pm\) 0.3 & 2.0 & 2.0 & 5.8 & 289.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiment of YOLO-AL on UA-DETRAC dataset. Figure 11: UA-DETRAC dataset sample example. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]\%** & **Params/M** & **FLOPs/G** & **FPS** \\ \hline Faster R-CNN & 86.4 & 41 & 180 & 13.1 \\ YOLOv5n & 87.6 & 2.5 & 7.1 & 201.6 \\ YOLOv6n & 86.8 & 4.2 & 11.8 & 272.1 \\ YOLOv7-tiny & 89.3 & 6.0 & 13.1 & 205.9 \\ YOLOv8n (baseline) & 90.0 & 3.0 & 8.1 & 252.0 \\ YOLOv9-tiny & 88.7 & 2.0 & 7.7 & 272.1 \\ YOLOv10n & 88.3 & 2.3 & 6.5 & 221.7 \\ Mobilenetv4-YOLO & 88.2 & 5.7 & 22.5 & 302.4 \\ EfficientDet-D0 & 87.6 & 3.9 & 3.2 & 90.7 \\ RT-DETR-L & 88.8 & 32 & 103.5 & 25.2 \\ YOLO-AL & 92.0 & 2.0 & 5.8 & 289.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of models on the UA-DETRAC dataset. Figure 13 shows a comparison of the confusion matrices of the YOLOv8n model and the YOLO-AL model. The YOLO-AL model achieved 1%, 3%, and 4% lower leakage detection rates than the YOLOv8n model in the Car category, Bus category, and Van category, respectively. In contrast, YOLO-AL achieves a lower leakage rate.