## 4 Results
### Comparison of YOLO Networks on a Custom Dataset  
In the first phase of the study, the performances of different YOLO networks were evaluated with the dataset created specifically for this study in order to determine the base model. Considering the resource-constrained device requirements, the small-scale models of the models from YOLOv5 to YOLOv10 were trained with a personal dataset (no augmentation), and metrics were obtained. Since some YOLO versions do not have small models, those available as open source were evaluated. Table 1 shows the YOLO models compared and the values obtained.  
As shown in Table 1, YOLOv5s seems to be a balanced model in terms of speed, accuracy, and efficiency, with 4.0 ms inference time, 0.79 [EMAIL_ADDRESS] accuracy, 15.9 GFLOPs computational cost, and 7.02 M parameters. The file size of 14.4 MB and low computational requirement make this model a suitable candidate to run on resource-constrained devices. The YOLOv6s model is relatively slow, with a 4.6 ms inference time, but yields 0.78 [EMAIL_ADDRESS] accuracy. However, due to 45.17 GFLOPs' computational cost and 40.7 MB file size, it may be too large and computationally expensive to run on resource-constrained devices.  
YOLOv8s is a satisfactory model in terms of inference time (5.4 ms). Although its accuracy (0.76 [EMAIL_ADDRESS]) is reasonable, it is relatively better than YOLOv6s in terms of its 28.4 GFLOPs computational cost and 22.5 MB file size. YOLOv7, which is among the larger models, is slower and has lower performance, with a 8.3 ms inference time and a 0.59 [EMAIL_ADDRESS] accuracy value. With a 105.1 GFLOPs computational cost and a 74.8 MB file size, it is far from being preferred for use in edge devices compared to other models.  
Although YOLOv9s offers one of the highest accuracy rates (0.78 [EMAIL_ADDRESS]) and has higher computational requirements than YOLOv5s, with 38.7 GFLOPs and 9.74 M parameters. Likewise, its inference time (13.2 ms) is also relatively long. The inference speed (11.9 ms) and accuracy level (0.74 [EMAIL_ADDRESS]) of the YOLO10s model are low compared to other models. Although it is a model that can be considered in scenarios requiring low resource usage, with its computational cost of 24.8 GFLOPs and 16.6 MB file size, it lags behind YOLOv5s and YOLOv9s in terms of accuracy. In light of all these, the YOLOv5s model stands out as the most suitable basic model for a system that will run on resource-constrained devices in terms of speed, accuracy, and computational cost.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Input Size**} & \multicolumn{2}{c}{**Inference Time**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{**FLOPs (G)**} & \multirow{2}{*}{**Param. (M)**} & \multirow{2}{*}{**File Size (MB)**} \\  & **(pixel)** & & & & & \\ \hline YOLOv5s & 640 \(\times\) 640 & 4.0 & 0.79 & 15.9 & 7.02 & 14.4 \\ \hline YOLOv6s & 640 \(\times\) 640 & 4.6 & 0.78 & 45.17 & 18.50 & 40.7 \\ \hline YOLOv7 & 640 \(\times\) 640 & 8.3 & 0.59 & 105.1 & 37.19 & 74.8 \\ \hline YOLOv8s & 640 \(\times\) 640 & 5.4 & 0.76 & 28.4 & 11.1 & 22.5 \\ \hline YOLOv9s & 640 \(\times\) 640 & 13.2 & 0.78 & 38.7 & 9.74 & 20.3 \\ \hline YOLO10s & 640 \(\times\) 640 & 11.9 & 0.74 & 24.8 & 8.06 & 16.6 \\ \hline \hline \end{tabular}  
* only the inference time, not including pre-process and NMS times.  
\end{table}
Table 1: Evaluation of results of different YOLO networks.