# SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection
## 6 Conclusions  
This research focuses on the vehicle detection task, with a particular application in traffic camera object detection. It mainly discusses how to improve the accuracy of vehicle detection and improve the algorithm performance metrics. The feature extraction capability of the original YOLOv7 algorithm was improved. We added the SE attention \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]/0.95 (\%)** & **Detection Speed** \\  & & & & & **per Frame (s)** \\ \hline SSD & 78.1 & 78.6 & 79.4 & 55.9 & 0.43 \\ Faster R-CNN & 80.4 & 86.4 & 84.2 & 61.8 & 0.84 \\ SPFNet & 89.1 & 91.6 & 90.4 & 69.7 & 0.68 \\ SE-YOLOv5 & 92.5 & 92.3 & 93.4 & 72.6 & 0.39 \\ YOLOv7 & 93.7 & 92.6 & 94.5 & 76.3 & 0.37 \\ Our model & 94.9 & 94.2 & 95.7 & 77.5 & 0.41 \\ \hline \hline \end{tabular}  
* The above indicators are expressed as percentages. Detection speed is expressed in seconds.  
\end{table} 
Table 4: Comparison between the SE-Lightweight YOLO and other traditional models. Figure 19: PR curve. Figure 20: Comparison of SE-Lightweight YOLO and YOLOv7 in the dataset. mechanism to the original YOLOv7 framework. We replaced the SPPCSPC with SPPFCSPC as the backbone of the network, which improved the feature extraction ability of the original YOLOv7 algorithm and effectively enlarged the sensory field of the model, thus improving the detection accuracy. While achieving more efficient and effective feature fusion, the network model's robustness is concurrently enhanced. The utilization of the Mosaic data augmentation technique further refines the feature extraction capabilities of the model. Experimental results demonstrate that SE-Lightweight YOLO significantly enhances detection precision and recall compared to YOLOv7 and other conventional object detection algorithms. However, the incorporation of modules and detection layers results in an increase in model parameters, leading to elevated floating-point operations (FLOPs) and consequently a higher model complexity. In the future of transportation applications, there is a growing inclination toward deploying real-time monitoring models on embedded systems, posing higher demands for improved accuracy and speed in small object detection. Given that current machine learning-generated data models tend to be larger, hindering seamless deployment and swift monitoring on embedded systems, this presents a challenge for application in such systems. Therefore, future research is deemed necessary to expand training datasets, explore lightweight network technologies, and engage in model pruning and knowledge distillation to reduce model size. This is essential for accelerating the application of research outcomes in transportation. Simultaneously, efforts can be put toward model compression to facilitate the deployment and real-time monitoring of models on embedded systems. This enhances the practical utility of the research and accelerates the application of research results in various domains. Drafting of the original manuscript and data preparation were conducted by C.N. ; [PERSON] took charge of the review and editing process; [PERSON] provided oversight and leadership for planning and executing the research activities. All authors have read and agreed to the published version of the manuscript. Shandong Provincial Natural Science Foundation, China: ZR2020 MF146, and the Innovation and Entrepreneurship Training Program for Students of Shandong Agricultural University: XJZD20202062. The selected datasets in this paper are public, and they can be freely downloaded at Kaggle ([[https://www.kaggle.com/datasets/yusufberksardoan/traffic-detection-project/](https://www.kaggle.com/datasets/yusufberksardoan/traffic-detection-project/)]([https://www.kaggle.com/datasets/yusufberksardoan/traffic-detection-project/](https://www.kaggle.com/datasets/yusufberksardoan/traffic-detection-project/)), accessed on 30 September 2023).