## 3 Architecture and System Evaluation
### Comparative Analysis and Evaluation of the YOLOv5 Inference Using the COCO Dataset  
As mentioned Table 1 presents map values for all \(q\) values used. Tables 1-4 present the results of measuring the YOLOv5 inference performance through 15 image sets taken from the validation set of the COCO Dataset [34]. Every image set consists of 100 images. The used YOLO models have different input image sizes and precisions. Moreover, they are implemented into various end devices with different inference machines to reveal sharp trade-offs. Firstly, in Table 2, we implemented the YOLO model with input image size 224 \(\times\) 224 \(\times\) 3 and INT8 precision into a Raspberry Pi 4 with the TensorFlow-Lite inference machine. Secondly, in Table 3, the Raspberry Pi 4 with 2 GB RAM and the google coral TPU using the edge TPU inference machine have also implemented the YOLO model with input image size 224 \(\times\) 224 \(\times\) 3 and INT8 precision. Finally, in Table 4, the last end device has implemented the YOLO model with input image size 320 \(\times\) 320 \(\times\) 3 and float32 precision. Comparing the results from the three tables we observe that the edge TPU inference machine executes the same workload almost 8 times faster than the TensorFlow-Lite inference machine with NN input size 320 \(\times\) 320 \(\times\) 3 and almost 2 times faster than the TensorFlow-Lite inference machine with NN input size 224 \(\times\) 224 \(\times\) 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Image Set** & **Execution Time (s)** & **Average CPU Utilization (\%)** & **Precision** & **Recall** & **mAP** \\ \hline Image Set1 & 23.3 & 27.5 & 0.67 & 0.42 & 0.47 \\ \hline Image Set2 & 23.6 & 27.9 & 0.69 & 0.34 & 0.41 \\ \hline Image Set3 & 23.5 & 28.1 & 0.66 & 0.40 & 0.47 \\ \hline Image Set4 & 23.1 & 26.8 & 0.57 & 0.38 & 0.41 \\ \hline Image Set5 & 23.0 & 24.8 & 0.74 & 0.44 & 0.52 \\ \hline Image Set6 & 23.4 & 26.2 & 0.55 & 0.42 & 0.45 \\ \hline Image Set7 & 23.3 & 26.5 & 0.61 & 0.42 & 0.46 \\ \hline Image Set8 & 24.0 & 27.2 & 0.67 & 0.40 & 0.46 \\ \hline Image Set9 & 23.0 & 28.2 & 0.49 & 0.44 & 0.45 \\ \hline Image Set10 & 23.2 & 28.9 & 0.57 & 0.40 & 0.45 \\ \hline Image Set11 & 23.5 & 27.2 & 0.60 & 0.35 & 0.41 \\ \hline Image Set12 & 23.1 & 27.4 & 0.55 & 0.38 & 0.42 \\ \hline Image Set13 & 23.3 & 27.7 & 0.59 & 0.38 & 0.44 \\ \hline Image Set14 & 23.3 & 27.5 & 0.57 & 0.36 & 0.43 \\ \hline Image Set15 & 23.1 & 27.8 & 0.61 & 0.39 & 0.44 \\ \hline \end{tabular}
\end{table}
Table 2: Evaluation of the YOLOv5 with TensorFlow-Lite Inference using the COCO Dataset and NN size \(224\times 224\times 3\).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Inference Machine** & **NN Size** & **mAP for \(q\) = 50** & **mAP for \(q\) = 75** & **mAP for \(q\) = 95** \\ \hline TensorFlow-Lite & 224 \(\times\) 224 \(\times\) 3 & 0.46 & 0.46 & 0.47 \\ \hline TensorFlow-Lite & 320 \(\times\) 320 \(\times\) 3 & 0.54 & 0.55 & 0.55 \\ \hline Edge-TPU & 224 \(\times\) 224 \(\times\) 3 & 0.44 & 0.44 & 0.45 \\ \hline \end{tabular}
\end{table}
Table 1: Map values for all \(q\) values used (1500 COCO images).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Image Set** & **Execution Time (s)** & **Average CPU Utilization (\%)** & **Precision** & **Recall** & **mAP** \\ \hline Image Set1 & 9.6 & 29.8 & 0.66 & 0.41 & 0.46 \\ \hline Image Set2 & 9.0 & 33.2 & 0.54 & 0.36 & 0.40 \\ \hline Image Set3 & 9.9 & 32.8 & 0.65 & 0.46 & 0.47 \\ \hline Image Set4 & 9.1 & 32.2 & 0.53 & 0.38 & 0.41 \\ \hline Image Set5 & 9.4 & 34.2 & 0.58 & 0.52 & 0.55 \\ \hline Image Set6 & 9.4 & 33.6 & 0.71 & 0.38 & 0.44