# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 4 Research and Analysis
### Comparison of Object Detection Performance  
This study compares the performance of two approaches for YOLO-based object detection on Android devices. The first approach is the On-Device method, which directly executes the YOLO model on the Android device. The second approach is the Edge Server  
Figure 12: Sequence diagram of a visual proximity detection and alert system. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Height (cm)**} & \multicolumn{3}{c}{**Location (cm)**} \\ \cline{2-4}  & **180** & **270** & **360** \\ \hline
153 & 2968.451 & 1275.108 & 600.8036 \\ \hline
158 & 2179.694 & 959 & 559.5476 \\ \hline
170 & 3261.687 & 1612.492 & 814.3333 \\ \hline
178 & 2478.534 & 1338.682 & 762.1111 \\ \hline
183 & 3269.375 & 1717.545 & 900.9821 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average facial size of each individual based on photographing position. based method, which transmits images from the device to an edge server, performs object detection through the YOLO model on the server, and then returns the results to the device for processing. The performance evaluation for the Edge Server-based approach involved measuring the image transmission time and model inference time across various image sizes. To directly compare the On-Device and Edge Server-based approaches, the total processing time was measured using images of identical dimensions. To accurately evaluate the performance differences between the Android device and the edge server, the experiments were conducted under consistent hardware and network conditions. The Android device used was a Galaxy S21 running Android 14, and object detection was performed using the YOLOv5 model. The network environment was configured with a 5 GHz Wi-Fi connection, and each measurement was repeated 10 times to ensure consistency and reliability in the experimental results. For performance evaluation, three key metrics were defined. The first metric was image transmission time, which measures the time taken to transmit images from the device to the edge server. The second metric was inference time, which measures the time required for the YOLO model to perform object detection. Lastly, the total processing time was defined as the sum of image transmission time, inference time, and result transmission time when using images of the same size, providing a comprehensive measure of overall system performance. The specific characteristics of the various image sizes tested during the evaluation are detailed in Table 4. As shown in Figure 13, the time required to transmit images from the device to the edge server in the Edge Server-based approach significantly varied depending on the image size. The shortest transmission time was recorded for Img7, with an average of 153 ms, while the longest transmission time was observed for Img2, averaging 821 ms. This variation suggests that transmission time is influenced by both the transmitted image size and network conditions. Next, the inference time of the YOLO model on the edge server is shown in Figure 14. The shortest inference time was 118 ms for Img7, while the longest inference time was 242 ms for Img2. This implies that inference time can also vary depending on the complexity and resolution of the input image. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Image ID**} & \multicolumn{3}{c}{**Image Properties**} \\ \cline{2-4}  & **Dimensions** & **Resolution (MP)** & **File Size (MB)** \\ \hline Img1 & 3024 \(\times\) 4032 & 12.00 & 4.08 \\ \hline Img2 & 6936 \(\times\) 9248 & 64.00 & 17.23 \\ \hline Img3 & 2268 \(\times\) 4032 & 9.00 & 2.56 \\ \hline Img4 & 5204 \(\times\) 9248 & 48.00 & 12.29 \\ \hline Img5 & 3024 \(\times\) 3024 & 9.00 & 2.63 \\ \hline Img6 & 6928 \(\times\) 6928 & 48.00 & 12.27 \\ \hline Img7 & 1816 \(\times\) 4032 & 7.00 & 2.35 \\ \hline Img8 & 4164 \(\times\) 9248 & 39.00 & 11.20 \\ \hline \end{tabular}
\end{table} 
Table 4: Specifications of test images used for performance evaluation of YOLO-based object detection methods.