## 6 Conclusions  
In this paper, a vehicle multi-object tracking method based on improved YOLOv5s and DeepSORT was introduced in detail. By adding an AIFI module to the backbone network of the YOLOv5s object detection algorithm, the speed of processing complex information and the vehicle detection accuracy under different levels of occlusion were improved. By improving the KF algorithm and the network structure of the DeepSORT multi-object tracking algorithm, the feature extraction ability of the vehicle was improved, the correlation between the data was enhanced, and the tracking effect of the vehicle was strengthened.  
The experimental data and results show that, compared with the original algorithm, the proposed algorithm can reduce the occurrence of missed detection and false detection when facing complicated environmental interference and blocked vehicles, and was more stable when continuously tracking the vehicles.  
It is important to note that the evaluation of performance depends on the selected metrics and specific application scenarios, and different tasks and data sets may obtain different results. Compared with other CNN architecture models, the improved YOLOv5s presented in this work has a lower volume and fewer parameters, which makes it more suitable for resource-constrained environments, such as mobile devices or embedded systems. The proposed algorithm also has some inherent limitations, such as inaccuracy when locating the object boundary and poor multi-scale target detection.  
In future work, we aim to create a more lightweight, faster traffic road vehicle detection model, while ensuring its accuracy and detection speed. We will try to use the SOTA models, in addition to YOLOv5, to further improve the stability and robustness of the vehicle detecting algorithm and to ensure its effectiveness in various complex traffic scenarios. This will enable the detection model to be deployed on embedded devices and edge computing platforms and run more quickly and efficiently, which is critical for real-time decision making in intelligent transportation systems.  
Conceptualization, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and Q.Z.; software, [PERSON] and [PERSON]. ([PERSON]); validation, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); formal analysis, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and Q.Z.; resources, [PERSON] and [PERSON]. ([PERSON]); data curation, [PERSON] and [PERSON]([PERSON]); writing--original draft preparation, [PERSON] and [PERSON]. ([PERSON]); writing--review and editing, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; visualization, [PERSON] and [PERSON]. ([PERSON]); supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Guangxi Science and Technology Program (Grant. No. Guike AD21238038), the National Natural Science Foundation of China (Grant. No. 62161031), and the Natural Science Foundation of Guangxi Province (GrantsNo. 2020 GXNSFAA297184).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the finding of the study are available from [PERSON] at [EMAIL_ADDRESS] upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.