## 3 Proposed Methods
### Performance Metrics  
In order to evaluate the efficiency of the object detection model in YOLOv5, YOLOv7, and YOLOv9, the mean average precision (mAP) value is used in performance measurements in this study. The mAP value is one of the preferred metrics for performance  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Description** & **Lower Limit** & **Upper Limit** \\ \hline lr0 & Initial learning rate & 0.0010 & 0.1000 \\ lrf & Final OneCycleLR learning rate & 0.1000 & 0.9000 \\ weight\_decay & Optimizer weight decay & 0.0005 & 0.0100 \\ Momentum & SGD momentum & 0.8000 & 0.9900 \\ warmup\_momentum & Warmup initial momentum & 0.8000 & 0.9500 \\ warmup\_epochs & Warmup epochs & 0.0000 & 1.0000 \\ Warmup\_bias\_lr & Warmup initial bias lr & 0.0000 & 0.1000 \\ box & Box loss gain & 0.0200 & 0.2000 \\ obj & Object loss gain & 0.2000 & 4.0000 \\ cls\_pw & Class BCLoss positive\_weight & 0.5000 & 2.0000 \\ cls & Class loss gain & 0.2000 & 4.0000 \\ Obj\_pw & Object BCLoss positive\_weight & 0.5000 & 2.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 12 hyperparameters of YOLOv5, YOLOv7, and YOLOv9.  
Figure 12: The workflow proposed in this study.  
measurements in instantaneous object detection in autonomous driving. In addition, there are studies that also consider the F1 score value for object detection in datasets with imbalances in labeled data. The F1 score is determined as the harmonic mean of the precision and recall values. In the open-access datasets used in our study, since the data are unbalanced, another performance measure, accuracy, is not taken into account in the performance measurement, as it can be misleading.  
While the algorithms detect objects, four different situations arise. True Positives (TPs) describe when the object is correctly predicted, True Negatives (TNs) describe when an existing object is incorrectly predicted, False Positives (FPs) describe when a non-existent object is predicted as if it exists, and finally, False Negatives (FNs) describe when the object exists but cannot be detected. These values allow the calculation of precision, recall, and the F1 score.  
**Precision:** The ratio of the correct prediction of the samples taken to all the samples taken is called precision. Mathematically, precision is expressed by the equation provided in (1).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
**Recall (Sensitivity):** The ratio of TP predictions to all relevant samples is called recall. Mathematically, Recall is expressed by the equation provided in (2).  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
**F1 Score:** The F1 score is the harmonic mean of the precision and recall and provides a balanced assessment of a model's performance while accounting for both false positives and false negatives. Mathematically, the F1 score is expressed by the equation provided in (3).  
\[\text{F1 Score}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{3}\]  
**mAP (Mean Average Precision):** The most important parameter used to measure and compare the performance of object detection algorithms, especially for vehicle and pedestrian detection, is mAP (mean average precision). The success of the model in detecting different objects is important. mAP determines the average precision performance of the model in all classes. This data show the general object detection performance of the model. Mathematically, mAP is expressed by the equation provided in (5).  
\[\text{AP}=\frac{1}{\text{m}}\sum_{\begin{subarray}{c}\text{r}=0,\\ \text{step}\ 0.1\end{subarray}}^{1,0}\text{p}(\text{r}) \tag{4}\]  
\[\text{mAP}=\frac{1}{\text{k}}\sum_{\text{i}}^{\text{k}}\text{AP}_{\text{i}} \tag{5}\]