# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 5 Conclusions  
In response to the robustness requirements of object detection in large-scale traffic monitoring systems, we propose an ETS-YOLOv7 based on YOLOv7. By integrating three CBAM attention modules into the network, the model focuses on more effective spatial and channel features, thus enhancing feature extraction capabilities in intricate traffic scenarios. The use of the WIoUv3 mitigates the influence of aspect ratio variations of vehicle  
Figure 6: Comparison of different detection methods. (**a**) Faster-rcnn; (**b**) YOLOv5s; (**c**) YOLOv8l; (**d**) YOLOv9s; (**e**) RT-DETR-L; (**f**) ETS-YOLOv7.  
targets on the loss calculation. In addition, we use the CLAN structure in the backbone network to reduce redundant computations while sustaining model accuracy, making the model more suitable for edge devices. Experiments on the UA-DTRAC dataset show that ETS-YOLOv7 outperforms the original YOLOv7 and other advanced methods, showing promise for applications in large-scale traffic monitoring systems. This research has broader implications for the advancement of intelligent transportation systems, particularly in the realms of traffic management, safety, and operational efficiency. The proposed ETS-YOLOv7 model, with its optimized computational efficiency and adaptability to edge devices, demonstrates a high degree of potential scalability across various real-time monitoring systems. This model's suitability for edge devices is particularly advantageous in scenarios where low latency and real-time processing are essential, such as autonomous vehicles and smart city infrastructure, which require the swift and accurate detection of traffic targets. The present study predominantly focuses on the detection of four-wheeled vehicles and above. Future work will aim to enhance the model's detection capabilities for smaller and more dynamic targets, such as motorcycles, non-motorized vehicles, and pedestrians. These smaller targets often present unique challenges due to their variability in size, speed, and behavior, as well as their frequent presence in congested urban environments. Improving the model's ability to accurately detect these entities, especially under adverse conditions such as poor lighting, heavy rain, or fog, is critical for ensuring comprehensive traffic surveillance. By extending detection capabilities to a wider range of targets and improving performance under challenging conditions, this research could lead to the development of more robust and reliable traffic monitoring systems. Such systems would not only help in reducing accidents and improving traffic flow but could also contribute to the design of advanced driver assistance systems and fully autonomous vehicles. Moreover, these advancements are expected to significantly enhance traffic safety and vehicle avoidance strategies, which are important fields of intelligent transportation systems.  
Conceptualization, D.Y. and Z.Y.; methodology, Z.Y.; formal analysis, D.Y. and Z.Y.; investigation, Z.Y., Y.W., and X.W.; resources, D.Y. and Y.W.; data curation, Z.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, D.Y. and Z.Y.; visualization, Z.Y.; supervision, D.Y. and Z.Y.; project administration, X.L.; funding acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major Program of National Social Science Foundation of China, grant number No. 23&ZD138.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors express their gratitude to [PERSON] and [PERSON] for their invaluable support during the preparation of the manuscript and data processing.  
The authors declare no conflicts of interest.