# TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5
## 4 Experiment and Result Analysis
### Result Analysis
Meanwhile, other indicators have no obvious changes. It can be seen that the TCE-YOLOv5 after FP16 quantization is more suitable for deployment in edge devices. Based on the analysis of Tables 4 and 5, it can be concluded that deploying models on resource-limited platforms requires simplified network architectures. Such a simplified network can reduce operator conflicts during TensorRT optimization. Compared with \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Models (FP32)**} & **P** & **R** & **mAP@50** & **mAP@50:95** & \multirow{2}{*}{**FPS**} \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline YOLOv5 & 94.5 & 87.5 & 93.6 & 69.0 & 30 \\ Improved-YOLOv5 [43] & 90.1 & 87.0 & 92.0 & 66.5 & 20 \\ TCE-YOLOv5 & 93.6 & 89.0 & 94.5 & 70.8 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of edge device model FP32 quantization. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Params (M)** & **Flops (G)** & \begin{tabular}{c} **mAP@50** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **mAP@50** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **mAP@50:95** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **P** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **R** \\ **(\%)** \\ \end{tabular} \\ \hline YOLOv5 & 7 & 16 & 93.5 & 66.3 & 93.9 & 87.8 \\ TCE-YOLOv5 & 5.7 & 12.6 & 93.4 & 66.5 & 93.2 & 87.9 \\ YOLOv7-tiny & 6 & 13.2 & 89.5 & 60.0 & 90.3 & 83.0 \\ Improved-YOLOv5 [43] & 5.8 & 13.7 & 92.0 & 66.1 & 92.7 & 86.3 \\ YOLOv8 & 11.1 & 28.7 & 97.2 & 75.6 & 96.6 & 92.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different models of the CCTSDB2021 dataset on PC. YOLOv5, the improved design of TCE-YOLOv5 demonstrates better adaptability for edge deployment, with enhanced performance across all metrics. Figure 8 shows the results of the different algorithms tested on the KITTI dataset. Specifically, Figure 8a shows the detection results of YOLOv5, Figure 8b shows the Improved detection effect of YOLOv5, and Figure 8c shows the results achieved by the proposed algorithm in this paper. Through comparative analysis, we can clearly observe that no matter YOLOv5 or Improved YOLOv5, there are cases of missing or wrong detection. In contrast, our algorithm demonstrated greater accuracy in detecting vehicles. Meanwhile, it can be seen that the detection accuracy of TCE-YOLOv5 is lower than that of YOLOv5 and Improved YOLOv5 for large targets, but the detection accuracy of TCE-YOLOv5 is higher than that of YOLOv5 and Improved YOLOv5 for medium targets and small targets. This is consistent with the previous theoretical analysis and the conclusions in Table 1.  
In order to further explore the specific contribution of each module to the performance of the basic model, seven ablation experiments were carefully planned in this paper, all of which were performed under KITTI dataset and experimental conditions. The results are detailed in Table 6. The analysis results show that each improvement measure has a positive impact on the model. In particular, the introduction of the T-C3 module significantly reduced the number of parameters in the model, while its negative impact on accuracy was minimal. In addition, C3 Res2 Net not only further reduces the parameter size, but also successfully improves the performance [EMAIL_ADDRESS] by 0.9 percentage points, improving the detection performance. In addition, C3 Res2 Net not only further reduces the parameter size, but also improves the detection accuracy for targets of different sizes, successfully enhancing the performance of mAP@50 Improved by 0.9 percentage points, improving  
Figure 8: Actual detection results, (**a**) YOLOv5; (**b**) Improved YOLOv5; (**c**) TCE-YOLOv5. The dataset is KITTI. detection performance. The addition of EIOU also played a positive role in the optimization of model performance, helping mAP@50 increase by 0.5%. These ablation experiments strongly verify the rationality and effectiveness of the proposed improvement measures.