## 5 Result and Discussion
### Evaluation Result  
#### 5.3.1 Comparative Analysis of YOLOv4 Model's Inference Performance in the COCO Dataset  
Tables 2 and 3 show the results of measuring inference performance through sample video by transforming YOLOv4-Native.weight and YOLOv4-Tiny.weight according to each deep learning framework in the COCO dataset. For TensorFlow in the COCO dataset, the CPU utilization of YOLOv4-Native is 20.80%, slightly lower than the 27.99% of YOLOv4-Tiny. However, the GPU utilization rate is 73.50%, which is about 1.4 times higher than the 52.19% of YOLOv4-Tiny. Because it utilizes more GPU computation for object detection, it is confirmed that the GPU utilization rate and the accuracy of human and vehicle object detection are 1.3 times higher. Due to the high utilization of the GPU, the measured power consumption using more power is also about 1.8 times higher. The model of YOLOv4-Native has 3 YOLO heads and 137 pretrained convolutional layers, and YOLOv4-Tiny has 2 YOLO heads and 29 pretrained convolutional layers. Therefore, due to the complexity and size of the model, it has a delay time of 127.82 ms for YOLOv4-Native and is measured to be about 4.5 times higher than the  
Figure 8: Some of the images captured during the inference performance measurement. 28.38 ms of YOLOv4-Tiny, so the accuracy of detecting an image object in one frame is high, but the processing speed is slow. TF-Lite, TF-TRT, and TRT in the COCO dataset, the model was converted by dividing the mixing precision by float32 and float16. In the case of TF-Lite, the accuracy of object detection for both float32 and float16 in YOLOv4-Native and YOLOv4-Tiny is similar to the previously measured results, but it can be seen that the latency is very high. This is because TF-Lite is a lightweight deep learning framework of TensorFlow, but has different purposes. TF-Lite is a deep learning framework specialized for mobile devices such as Android and IOS; it does not apply to the Jetson AGX Xavier that is currently used, so the GPU usage rate does not work below 1% [41]. Therefore, the GPU usage rate is low, and the power consumption is also the lowest among all deep learning frameworks because the GPU does not operate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Framework** & \multirow{2}{*}{**Precision**} & **Average** & **Average** & **Average** & **Average** & **Average** \\ (YOLOv4-Native in the COCO Dataset) & & **CPU** & **GPU** & **Accuracy (\%)** & **Latency (ms)** & **Power (W)** \\ \hline TensorFlow & Float32 & 20.80 & 73.50 & Person: 43.53 & & \\  & & & & Car: 71.29 & 127.82 & 28.24 \\ \hline TF-Lite & & 18.95 & 0.91 & Person: 42.66 & & \\  & & & & Car: 71.12 & & \\ TF-TRT & Float32 & 24.49 & 67.41 & Person: 42.55 & & \\  & & & & Car: 71.29 & 61.63 & 15.87 \\  & & & & Person: 43.26 & & \\  & & & & Car: 72.18 & & \\ \hline TF-Lite & & 17.99 & 0.62 & Person: 40.13 & & \\  & & & & Car: 71.87 & & \\ TF-TRT & Float16 & 29.65 & 47.49 & Person: 43.55 & & \\  & & & Car: 71.27 & & 32.15 & 11.79 \\  & & & & Person: 43.31 & & \\  & & & & Car: 72.47 & & 27.92 & 22.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of YOLOv4-Native.weights’s inference performance in the COCO Dataset.