# Research on Vehicle Target Detection Method Based on Improved YOLOv8
## 4 Experiments
### KITTI 2D  
The KITTI 2D dataset [35] developed in 2012, is a crucial benchmark in computer vision. The dataset collects data from real traffic scenes using specially configured acquisition vehicles, covering a variety of scenarios and conditions, and is widely used in the evaluation of object detection, image segmentation, and other visual tasks. Due to its rich annotation information and diverse scene settings, the KITTI 2D dataset has become one of the most commonly used computer vision algorithm evaluation standards in the world. The KITTI 2D dataset uses the image from the left camera and contains 7481 labeled images divided into 8:1:1 ratios, including 5985 training sets, 748 validation sets, and 748 test sets. The categories of labels are Car, Van, Truck, Tram, Pedestrian, Person (sitting), Cyclist, and Misc. In this experiment, Misc, Person (sitting), and Pedestrian are deleted. Figure 6 shows an example of the KITTI 2D dataset. To more accurately assess the model proposed in this paper and validate the reliability and effectiveness of the algorithm, all experiments in this chapter are conducted in a standardized operating environment. The specific environment configuration and hyperparameter settings are shown in Tables 1 and 2. This experiment compares the YOLO-AL model with classic target detection models to evaluate its performance, including Faster R-CNN, YOLOv5n, YOLOv6n, YOLOv7-tiny [36], YOLOv8n, YOLOv9-tiny [37], YOLOv10n [38], MobileNetv4-YOLO, EfficientDet-D0, and RT-DETR-L. Detailed detection results are presented in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Configuration** \\ \hline CPU & 16 vCPU Intel(R) Xeon(R) Platinum 8481C \\ GPU & RTX 4090D(24 GB) \\ Operating system & Ubuntu20.04 \\ Memory & 80 GB RAM \\ Deep learning frameworks & PyTorch 1.11.0 \\ Programming language & Python 3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental environment configuration. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Epochs & 200 \\ Train batch size & 32 \\ Test batch size & 1 \\ Optimizer & SGD \\ Initial learning rate & 0.01 \\ Momentum & 0.937 \\ Weight decay coefficient & 0.0005 \\ Numerical precision & FP32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter settings. Figure 6: KITTI 2D 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 & 91.1 & 41 & 180 & 13.5 \\ YOLOv5n & 92.1 & 2.5 & 7.1 & 207.8 \\ YOLOv6n & 91.3 & 4.2 & 11.8 & 280.5 \\ YOLOv7-tiny & 92.7 & 6.0 & 13.1 & 212.3 \\ YOLOv8n(baseline) & 93.4 & 3.0 & 8.1 & 259.8 \\ YOLOv9-tiny & 92.9 & 2.8 & 4.5 & 280.5 \\ YOLOv10n & 93.2 & 2.3 & 6.5 & 228.7 \\ MobileNetv4-YOLO & 92.7 & 5.7 & 22.5 & 311.7 \\ EfficientDet-D0 & 92.3 & 3.9 & 3.2 & 93.5 \\ RT-DETR-L & 93.2 & 32.0 & 103.5 & 25.9 \\ YOLO-AL & 94.5 & 2.0 & 5.8 & 290.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of models on the KITTI 2D dataset. As presented in Table 3, The lightweight vehicle detection model YOLO-AL achieved the highest [EMAIL_ADDRESS] in the KITTI 2D dataset, which is 94.5%. Although FLOPs are slightly larger than EfficientDet-D0 and the FPS is slightly lower than Mobilenetv4-YOLO, it has significant advantages in other aspects. Compared to the original model YOLOv8n, the [EMAIL_ADDRESS] of YOLO-AL increased by 1.1%, the parameter count was reduced by 33%, and FLOPs reduced by 28%, while FPS increased by 12%. To evaluate the effectiveness of each component of YOLO-AL, a detailed ablation study was performed on the KITTI 2D dataset while ensuring that all experimental settings and parameters were consistent. Table 4 shows the comparison data from the ablation experiment. The evaluation indicators for the experiment are [EMAIL_ADDRESS], the number of parameters, FLOPs, and FPS. The addition of each module improves the model detection accuracy and speed while effectively reducing the number of parameters and FLOPs.