## 5 Conclusions  
In this article, a real-time object detection solution based on edge cloud system for airport apron operation surveillance video is proposed, which includes lightweight detection model Edge-YOLO, edge video detection acceleration strategy, and cloud-based detection results verification mechanism. The lightweight detection model is based on YOLOV5s. By replacing the backbone with ShuffleNetv2, cropping the channel of the detect head, and adding coord attention, the model size of Edge-YOLO is reduced by more than 90% compared with YOLOv5s, greatly reducing the requirements for deployment equipment. The detection speed of Edge-YOLO on Raspberry Pi 4B is increased by 3.37 times, and its accuracy loss does not exceed 5% by testing on a dataset composed of more than 20,000 real-world airport apron scenes. Edge video detection acceleration  
Figure 13: The mAP and time cost of Edge-YOLO-CA with acceleration strategy on raspberry 4B. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vedio Id** & **T** & **1 FPS** & **2 FPS** & **3 FPSs** & **4 FPS** & **SFPS** \\ \hline \multirow{3}{*}{1} & [EMAIL_ADDRESS] (\%) & 90.07 & 89.78 & 88.43 & 87.35 & 85.03 \\  & time consumption & 35 min 46 s & 23 min 28 s & 22 min 37 s & 22 min 27 s & 22 min 18 s \\ \hline \multirow{3}{*}{2} & [EMAIL_ADDRESS] (\%) & 88.49 & 88.21 & 86.51 & 84.53 & 82.66 \\  & time consumption & 32 min 49 s & 21 min 47 s & 18 min 34 s & 17 min 35 s & 17 min 29 s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The mAP and time consumption of Edge-YOLO-CA with acceleration strategy on raspberry 4B. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vedio Id** & **T** & **1 FPS** & **2 FPS** & **3 FPS** & **4 FPS** & **SFPS** \\ \hline \multirow{3}{*}{1} & [EMAIL_ADDRESS] (\%) & 93.47 & 93.43 & 93.45 & 93.39 & 93.51 \\  & cloud detect num & 7973 & 8797 & 14.764 & 18.874 & 21.564 \\  & cloud detect & 8.96\% & 9.88\% & 16.58\% & 21.20\% & 24.22\% \\ \hline \multirow{3}{*}{2} & [EMAIL_ADDRESS] (\%) & 92.57 & 92.49 & 92.61 & 92.54 & 92.65 \\  & cloud detect num & 8361 & 9748 & 15,732 & 19,746 & 22,893 \\ \cline{1-1}  & cloud detect & 9.68\% & 11.28\% & 18.21\% & 22.86\% & 26.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of Edge-YOLO-CA with acceleration strategy and edge–cloud cooperation on Edge-Cloud System. realizes further detection acceleration of Edge-YOLO on airport surveillance video by motion information inference by segmenting the video, then using _IOU_ matching and improved T-frame difference method to extract the motion information of objects, and finally combining the detection results of Edge-YOLO on key frames to quickly generate the detection results of the entire segment. The cloud-based detection results verification mechanism uses a non-lightweight detection model with higher accuracy to verify and correct the results generated by the edge to improve the overall detection accuracy and reduce the proportion of cloud intervention through a multi-level intervention mechanism. Their feasibility is verified by experiments on two real airport apron surveillance videos that are fully annotated and contain the complete operation process. In the future, we will focus on designing more flexible edge-cloud cooperation strategies, and strengthen the versatility of the method and extend it to a wide range of video surveillance scenarios. [PERSON] proposed the network architecture design and the framework of Edge-YOLO. [PERSON] proposed the strategy of video detection acceleration and edge-cloud Cooperation. [PERSON] and [PERSON] performed the experiments, analyzed the experimental data, and wrote the article. All authors have read and agreed to the published version of the manuscript. This research was funded by the Fundamental Research Funds for Central Universities of the Civil Aviation University of China, grant number 3122021088. Not applicable. We would like to thank the anonymous reviewers and the editor-in-chief for their comments to improve the article. Thanks also to the data provider.