# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 5 Result and Discussions  
### YOLO Train Result  
In this section, we will compare YOLOv8s as the chosen model to other algorithms in the YOLOv8 family, such as YOLOv8m, YOLOv8l, and YOLOv8x, in order to assess the performance of the chosen LED detection model. This study assesses the precision, speed, GFLOPs, and weight of the most recent detection algorithms. Based on the evaluation indicators described in this paper, Table 1 compares the state-of-the-art LED detection algorithms with the proposed YOLOv8s. Table 1 provides a summary of the LED detection model proposed in this investigation, based on YOLOv8. In terms of mAP0.5, mAP0.5:0.95, and precision performance, our analysis indicates that there is no significant difference between the YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x models. The YOLOv8x achieved the highest overall performance with a slight difference. However, there is a positive correlation between processing time, GFLOPs, and the weight of the model, whereas larger GFLOPs and heavier weight will increase processing time significantly. In contrast, the batch size has a negative correlation with the processing time, where a smaller batch size will increase the processing time. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Batch Size** & **mAP0.5:95** & **mAP0.5** & **Speed (ms)** & **Precision** & **GFLOPs** & **Weight** \\ \hline \multirow{4}{*}{X} & 32 & 0.86817 & 0.999 & 4.566 & 0.999 & 258.133 & 136.7 \\  & 16 & 0.86744 & 0.99744 & 8.206 & 0.99744 & 258.133 & 136.7 \\  & 8 & 0.86744 & 0.99744 & 8.917 & 0.99744 & 258.133 & 136.7 \\ \hline \multirow{4}{*}{L} & 32 & 0.86964 & 0.99498 & 2.825 & 0.999 & 165.412 & 87.6 \\  & 16 & 0.86936 & 0.995 & 3.103 & 0.99999 & 165.412 & 87.6 \\  & 8 & 0.86519 & 0.995 & 6.627 & 0.99959 & 165.412 & 87.6 \\ \hline \multirow{4}{*}{M} & 32 & 0.86341 & 0.995 & 1.954 & 0.998 & 79.072 & 52 \\  & 16 & 0.86627 & 0.995 & 3.301 & 0.99799 & 79.072 & 52 \\  & 8 & 0.86634 & 0.99495 & 4.827 & 0.99725 & 79.072 & 52 \\ \hline \multirow{4}{*}{S} & 32 & 0.86004 & 0.995 & 1.25 & 0.99 & 28.651 & 22.5 \\  & 16 & 0.86169 & 0.99497 & 1.834 & 0.999 & 28.651 & 22.5 \\ \cline{1-1}  & 8 & 0.86453 & 0.995 & 2.536 & 0.997 & 28.651 & 22.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison performance of YOLOv8 family using our LED array detection dataset. Furthermore, the YOLOv8s with batch size 32 outperformed all the other models in terms of processing time, weight, and GFLOPs values of 1.25 ms, 22.5 MB, and 28.65, respectively, with comparable mAP and precision. Table 2 compares the performance of the YOLOv8 detection algorithm with YOLOv5. In this comparison, we set the batch size to 32 and the number of training epochs to 1000, maintaining the same specifications. According to the literature [39], the performance of both models is said to be very similar. Therefore, we conducted a retest based on that literature. Although the performance of both YOLO types is said to be very similar, we found significant differences in terms of processing time and the size of the weights produced by each YOLO. We found that YOLOv8 performs better compared to YOLOv5, with a processing time of 1.25 ms and a weight size of 22.5 MB. Therefore, YOLOv8s is the most suitable model to implement on resource-constrained devices in terms of processing performance and portability. Figure 9 depicts the comparison of the YOLOv8 training process in terms of mAP0.5 and mAP0.5.0.95. Figure (a)a shows the training process over 300 epochs and saturated after approximately 75 epochs. Furthermore, even though all the models achieve similar mAP results, YOLOv8s convergence is slightly faster than the other model. On the other hand, Figure (b)b depicts a lower mAP compared to Figure (a)a, with a maximum value of 0.86964. From the figure, we can see that the model's convergence is slower and saturated after 100 epochs.