# TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5
## 5 Conclusions  
Object detection is an important component of autonomous driving, The deployment of object detection models based on deep neural networks must consider the limited resources of the vehicle platform. Prior methods have either focused on improving detection performance and could not be deployed on vehicle platforms due to the large network, or its deployment performance on vehicle platforms was too low. In this paper, a light-weight object detection model named TCE-YOLOv5 is introduced, specially designed for minimizing the number of model parameters and computational complexity while ensuring the accuracy of object detection. By replacing C3 in the backbone network with T-C3 and C3 in the neck with Res2 Net, the number of parameters and computational complexity are greatly reduced. In order to balance the problem of decreased accuracy in detecting large targets caused by changes in the backbone network and neck, the EIOU loss function is introduced. The experimental results show that TCE-YOLOv5 is slightly lower than the original YOLOv5 in precision, but all other performance indicators are higher than the original YOLOv5 when performing on the CCTDSB2021 and KITTI datasets. Especially, as a key performance indicator, the mAP@50:90 of TCE-YOLOv5 is always greater than that of the original YOLOv5 in all tests. This is because our proposed modification aims to improve the detection accuracy of small and medium size objects by sacrificing the detection accuracy of large size objects, as small and medium targets detection are more difficult and important for autonomous driving. Due to the reduction in parameter count and computational complexity, the TCE-YOLOv5 runs at a higher FPS (61 frame/s--a 15% increase compared to the original YOLOv5) on the Jetson Xavier NX hardware platform. These results fully prove the superiority of TCE-YOLOv5 in the field of lightweight object detection, and provide a strong guarantee for the real-time and high efficiency of automatic driving systems. In future work, we plan to further explore the possibilities of improving its accuracy. Additionally, we will leverage the resources of State Key Laboratory of Intelligent Vehicle Safety Technology to increase the number of real-world testing scenarios. This will help us gain a more comprehensive understanding of the model's performance in practical applications and provide valuable feedback for further improvements.  
[PERSON]. ([PERSON]) put forward the initial conception of this study and led the design of the research plan. [PERSON] undertook most of the data collection work in this study. [PERSON] recorded the experimental data and phenomena, and conducted in-depth analysis and processing of the experimental results. [PERSON] and [PERSON]. ([PERSON]) gave full play to their professional advantages in research methods and optimized and improved the research methods. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**T-C3** & **C3 Res2 Net EIOU** & **P** & **R** & **mAP@50** & **mAP@50:95** & **Params (M)** \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline  & & & 94.4 & 87.1 & 93.5 & 68.8 & 7 \\ ✓ & & & 93.6 & 87.0 & 93.5 & 68.4 & 6.2 \\  & ✓ & & 94.2 & 90.1 & 94.4 & 71.9 & 6.5 \\  & & ✓ & 94.1 & 87.6 & 94.0 & 70.0 & 7 \\ ✓ & ✓ & & 94.4 & 88.8 & 94.2 & 70.3 & 5.7 \\ ✓ & & ✓ & 94.3 & 88.6 & 94.2 & 70.4 & 6.2 \\ ✓ & ✓ & ✓ & 94.1 & 88.8 & 94.5 & 70.8 & 5.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments of the KITTI dataset on PC.  
**Funding:** This work was funded by the State Key Laboratory of Intelligent Vehicle Safety Technology Open Project (IVSTSKL-202328).  
**Data Availability Statement:** No new data were created in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.