## 3 Experimental Test and Result Analysis
### Experimental Test
#### 3.4.5 Integrated Experiment  
The Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement was selected as the experimental site.  
In order to realize the object detection function, this paper deploys deep learning object detection technology to the robot equipment. For this reason, this paper develops an object detection function package based on ROS-YOLOv5-FleetEMA, enters the src directory in the working space catkin_ws, and opens the terminal; input conda activate yolo, enter the virtual environment, enter the function package directory, enter sudo pip install -r requirements.txt, and install the object detection-related dependency library.  
After the installation is completed, enter the roslaunch yolov5_ros yolo.launch command and start the usb_cam and the object detection function based on ROS-YOLOv5-FleetEMA at the same time. The usb_cam is a package used for interacting with the USB camera. This package allows users to subscribe to camera image topics and publish them  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **EMA** & **C3 Ghost** & **MPDIoU** & **mAP @ 0.5** & **mAP @ 0.5-0.95** & **GFLOPs** & **Param** \\ \hline YOLOv5-lite & - & - & - & 0.768 & 0.515 & 3.8 & 1,566,561 \\ YOLOv5-lite + EMA & + & - & - & 0.776 & 0.534 & 3.8 & 1,566,575 \\ YOLOv5-lite + C3 Ghost & - & + & - & 0.769 & 0.519 & 3.3 & 1,328,617 \\ YOLOv5-lite + MPDIoU & - & - & + & 0.772 & 0.522 & 3.8 & 1,566,561 \\ YOLOv5-lite + EMA + C3 Ghost & + & + & - & 0.783 & 0.539 & 3.3 & 1,328,617 \\ YOLOv5-lite + EMA + MPDIoU & + & - & + & 0.778 & 0.536 & 3.8 & 1,566,575 \\ YOLOv5-lite + C3 Ghost + MPDIoU & - & + & + & 0.771 & 0.533 & 3.3 & 1,328,617 \\ ROS-YOLOv5-FleetEMA & + & + & + & 0.792 & 0.558 & 3.3 & 1,332,471 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experimental results.  
Figure 8: Ablation experimental results.  
to ROS, allowing them to use USB cameras in ROS. By subscribing to the image topic published by usb_cam, we employ cv-bridge to transform ROS image messages into the OpenCV image format. Within the callback function, we execute YOLOv5 object detection on the transformed image, subsequently convert the processed image back into ROS image messages, and publish them to a new YOLOv5 topic.  
At this point, open the ROS-based object detection service platform, set the IP address of the car, and then connect the device; by using the QT button to subscribe to newly established YOLOv5 topics with just one click, the results will be displayed on the ROS-based object detection platform, as shown in Figure 9.  
Through experimental analysis, the application effect of the ROS-YOLOv5-FleetEMA model proposed in this paper in the ROS robot system is verified. The model not only performs well in a resource-constrained environment but also integrates with a ROS-based object detection platform to achieve efficient and fast object detection. Specifically, the system can accurately identify and track multiple targets, such as pedestrians, monitors, etc. When the car maintains a speed of 0.5 to 1.5 m per second, it takes 34.4 milliseconds to identify the object, up to 30 FPS, ensuring the fluency of the detection process. This optimization not only improves the robot's perception ability in complex environments but also provides strong support for further decision-making and execution.