## 3 Experiment Setup and Evaluation
### Results of Evaluated Metrics  
In this section, the results of the machine learning algorithms are analyzed with respect to \(P\), \(R\), \(F_{1}\)-score, \(AP_{50}\), and average detection time. Table 3 show the \(P\), \(R\), and \(F_{1}\)-score results of the Viola-Jones, ACF, and deep learning detectors. The results shown by these algorithms concerning precision are mainly due to the presence of \(FP\). Their authors have debated this large number of \(FP\) as a disadvantage, because they detect the same object multiple times. Multiple detections occurred because of the use of sliding windows at different scales. The Haar-like and HOG features generally  
Figure 12: Results of the detectors in database. (**a**) ACF detector. (**b**) Viola-Jones detector. (**c**) Faster R-CNN detector. (**d**) SSD detector. (**e**) YOLO v4 detector. Figure 13: Results of the detectors out of the database. (**a**) ACF detector. (**b**) Viola-Jones detector. (**c**) Faster R-CNN detector. (**d**) SSD detector. (**e**) YOLO v4 detector. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Metrics (\%)**} \\ \cline{2-5}  & \(P\) & \(R\) & \(F_{1}\)**-Score** \\ \hline \multirow{3}{*}{Viola-Jones} & Haar-like & 40 & 100 & 57.1 \\  & LBP & 82.2 & 85 & 83.5 \\  & HOG & 10 & 100 & 18.1 \\ \hline \multirow{3}{*}{ACF} & Exp-1 & 54 & 75.7 & 63.1 \\  & Exp-2 & 44.7 & 77.8 & 63.1 \\ \cline{1-1}  & Exp-3 & 41.8 & 78.6 & 54.6 \\ \cline{1-1}  & Exp-4 & 39.8 & 78.2 & 52.7 \\ \hline Faster R-CNN & & 97 & 93 & 94.9 \\ \hline SSD & & 99.6 & 90 & 94.5 \\ \hline YOLO v4 & & 99.9 & 99.7 & 99.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics of detectors. performed worse than the LBP features. The increase in the ROIs and number of weak classifiers in the ACF algorithm did not yield good results concerning precision; however, a slight increase in recall was observed. The LBP feature in the Viola-Jones algorithm showed the best results for all metrics compared to the analyzed classic algorithms. This feature efficiently describes the characteristics of the object of interest. These algorithms require a post-processing stage to decrease \(FP\) and thus increase the precision. In general, deep learning algorithms achieve better results than classic algorithms. This was expected because of their excellent generalization capabilities. In these detectors, different types of networks, such as ResNet-50, MobileNetV2, and CSPDarkNet53, are used as backbones. The Faster R-CNN algorithm achieved the lowest precision value owing to the presence of \(FP\). These algorithms have fewer \(FPs\) than the classic algorithms owing to the Non-Maximum Suppression (NMS) technique. This technique helps eliminate redundancy and reduce the number of overlapping bounding boxes. The SSD algorithm produced the worst results in terms of recall, which might have been influenced by using MobileNetV2 as a backbone. The YOLO v4 algorithm displayed excellent precision, recall, and \(F_{1}\)-score results. The \(AP_{50}\) metric is widely used to evaluate object detection algorithms. This study used this metric in deep learning algorithms because they achieved the best results for \(P\), \(R\), and \(F_{1}\)-score. Figure 14 shows the \(AP_{50}\). The \(AP\) provides a single performance metric that summarizes the shape of the precision-recall curve. All deep learning algorithms achieved an \(AP_{50}\) above 89%, with YOLO v4 achieving the highest value at 99.7%. This metric is important because it shows the average precision of the model when the recall is 50%. Figure 15 shows the precision vs. recall curve for the deep learning algorithms under analysis. This curve is a valuable tool for understanding the performance of the trained classification models. It also offers insight into how the performance of the models fluctuates across different thresholds. Given these factors, it is apparent that the YOLO v4 algorithm outperforms the other algorithms.