# TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5
## 4 Experiment and Result Analysis
### Result Analysis  
Table 2 shows the experimental results of different algorithm models in the KITTI dataset. In other words, the TCE-YOLOv5 algorithm is compared with YOLOv5, YOLOv7-tiny [19], YOLOv8 [42] and Improved YOLOv5 [43]. As can be seen from Table 3, the above five algorithms all show good performance. Among them, TCE-YOLOv5 is the algorithm with the fewest parameters and calculation. Compared with the benchmark model YOLOv5, the number of parameters is reduced by 18.5%, the calculation is reduced by 21%, and mAP@50 is increased by 1.0%. Compared with other algorithm models, the indicators of YOLOv7-tiny and Improved YOLOv5 are weaker than those of TCE-YOLOv5. Although YOLOv8 is higher than TCE-YOLOv5 in terms of accuracy, the parameter count and calculation amount of YOLOv8 are much higher than that of TCE-YOLOv5, so YOLOv8 is not suitable for deployment on edge devices. Overall, TCE-YOLOv5 is the most balanced and suitable for deployment in edge devices. To further demonstrate the superiority of TCE-YOLOv5 in autonomous driving detection, we performed an experimental evaluation on the CCTSDB2021 dataset. The CCTSDB2021 dataset provides traffic sign classification. Compared with the KITTI dataset, the CCTSDB2021 dataset has more small targets and is more challenging. Table 3 shows the detection capabilities of proposed method and other methods. It can be seen that the mAP@50 of TCE-YOLOv5 is still higher than that of YOLOv7-tiny and Improved YOLOv5, but it is almost the same as that of the benchmark model YOLOv5 and much lower than that of YOLOv8. However, considering the parameter count and the amount of calculations, TCE-YOLOv5 is still the best choice for edge devices. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Models** & **Params (M)** & **Flops (G)** & \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 & 68. 8 & 94.4 & 87.1 \\ TCE-YOLOv5 & 5.7 & 12.6 & 94.5 & 70.8 & 94.1 & 88.8 \\ YOLOv7-tiny & 6 & 13.2 & 92.8 & 64.5 & 92.2 & 86.2 \\ Improved-YOLOv5 [43] & 5.8 & 13.6 & 91.6 & 66.5 & 90.5 & 86.9 \\ YOLOv8 & 11.1 & 28.7 & 95.2 & 78.0 & 95.0 & 90.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different models of the KITTI dataset on PC. Considering that TCE-YOLOv5 is designed for embedded device deployment, we chose Jetson Xavier NX, quantized the model FP32 and FP16 using TensorRT, and performed comparative experiments. FP32, a 32-bit single-precision floating point number, is one of the most commonly used numerical representations in deep learning. FP16 is a 16-bit half-precision floating point number. Compared with FP32, it occupies half of the memory and bandwidth, and is often used in inference processes to speed up calculations and reduce memory usage. Using FP16 can significantly reduce the time to model inference, while in most cases having less impact on model accuracy. In the same test environment, we used the same dataset for evaluation and compared it with the benchmark model YOLOv5 and Improved YOLOv5. The experimental results of FP32 quantization are shown in Table 4.  
As can be seen from Table 4, TCE-YOLOv5 shows excellent performance in multiple performance indicators. Except having a \(p\) value slightly lower than YOLOv5, the other indicators are better than YOLOv5 and Improved YOLOv5. Especially in terms of FPS, TCE-YOLOv5 reached 32 frames, which is only 2 frames higher than YOLOv5, but it also reflects that the network structure of TCE-YOLOv5 is better than YOLOv5, and can provide faster response when processing images or videos. The experimental results of FP16 quantization are shown in Table 5.  
As can be seen from Table 5, after FP16 quantization, the FPS of TCE-YOLO is significantly Improved, which is 8 frames higher than the benchmark model YOLOv5, and 10 frames higher than the Improved YOLOv5.