## 4 Model Application on Air Cruiser
### Experiments  
Since the video transmission rate and the inference speed of flight control base station are not capable of real-time operation, the experiment in this chapter is aimed at collecting image samples from the air cruiser view, segment offline instances, and solve coordinates. First of all, the experiment collects video samples from the view angle of the cruiser at a height of 10 m from the ground and then records coordinates of the target position of the video example with reference to the handheld GPS positioning instrument. The shooting horizontal angle \(\phi\) is \(-90^{\circ}\), the tilt angle \(\kappa\) is maintained at \(0^{\circ}\), and the vertical right angle \(\omega\) is set at \(90^{\circ}\); that is, the shooting is always perpendicular to the ground and facing the west using a focal length of \(f=70\) mm and the frame rate is 30 fps. In the interface, Long is the coordinate longitude value and Lat is the latitude value. Among them, the GPS coordinates where the cruiser's viewing angle is taken are \((102^{\circ}51^{\prime}18.864^{\prime\prime},24^{\circ}50^{\prime}53.124^{ \prime\prime})\). The sample of cyclists located at GPS coordinates \((102^{\circ}51^{\prime}17.031^{\prime\prime},24^{\circ}50^{\prime}53.840^{ \prime\prime})\) is set to carry out the experiment, and the distance of the sample from the ground shooting coordinates is about 60 m. The ONNX conversion of CWVIS and the innovative model MLGW-VIS proposed in this study is carried out and deployed in the flight control software. Video instance segmentation and GPS coordinate positioning are performed on all instances in the sample, and the real GPS coordinate values are recorded by the handheld positioning instrument. The segmentation visualization results are shown in Table 5. It can be seen that the innovative model MLGW-VIS proposed in this study has obvious advantages in the segmentation effect. Especially when the target instance is far away and the target image is small, the segmentation is more complete and the edge is more detailed, and the instance identification of the bicycle and the human body is accurate. The experimental results of the GPS coordinate solution are shown in Table 5. It can be seen that there is a larger coordinate error between the weakly supervised baseline model and the coordinates  
Figure 10: Target localization geometric relationship diagram. of reference personnel in the GPS solution results, while our MLGW-VIS proposed in this paper provides a better mask area after optimization, thus making the example positioning results more accurate. In addition, we perform segmentation visualization and target positioning on a continuous motion video data of the car instance and demonstrate our positioning effect by indexing GPS coordinates in a real electronic map. The relevant experimental results of the ONNX model using MLGW-VIS are presented in this paper as shown in Table 6. From the segmentation visualization results, it can be seen that our model still obtains excellent mask prediction quality when the instance is obscured, and it is finely segmented during continuous motion. According to the GPS coordinates obtained by inference, we index and mark the anchor points from the electronic map with the blue mark showing the positioning results obtained by the program in this chapter and the green arrow mark showing the location on the actual road. It can be seen that the positioning results obtained in this chapter basically maintain the same map error as the actual road position, and the motion trajectory is basically consistent. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ONNX Model** & **Segmentation** & **GPS** & **Target Location** \\  & **Visualized Results** & **Coordinates** & **on Map** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Target instance ONNX model deployment segmentation visualization and the corresponding map mark.