## 3 Results and Discussion  
### The Transverse Crack Detection Results of the YOLOv2 Detector  
The YOLOv2 detector was tested on 984 images, and its performance is represented in Figure 5 for three different optimizers (SGDM, Adam, and RMSProp) according to hyperparameter configuration (Table 2) and three different threshold values, \(t\) = 0.25, \(t\) = 0.50, and \(t\) = 0.75. Figure 5: _Cont._  
It is evident that the threshold value significantly affects both AP and LAMR. As the threshold value t increases, the area under the precision-recall curve decreases, indicating a lower value of AP, which corresponds to the increasing value of LAMR. The detector poorly performs when the threshold value is \(t=0.75\), \(AP@.75\) is approaching zero, and the LAMR value fluctuates between 0.94 and 0.96 for all optimizers. The Adam optimizer with threshold values \(t=0.25\) and \(t=0.50\) leads to \(AP@.25=0.89\) and \(AP@.50=0.62\) with LAMR = 0.25 and LAMR = 0.58, respectively (Table 2). Although the \(AP\) values of all optimizers are similar, Adam performs better than SGDM and RMSProp. Examples of the outcomes of the YOLOv2 detector with ResNet-50 for two same images with transverse cracks and SGDM, Adam, and RMSProp optimizers are shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Optimizer**} & \multicolumn{2}{c}{**t = 0.25**} & \multicolumn{2}{c}{**t = 0.50**} & \multicolumn{2}{c}{**t = 0.75**} \\ \cline{2-7}  & **AP** & **LAMR** & **AP** & **LAMR** & **AP** & **LAMR** \\ \hline SGDM & 0.8646 & 0.2757 & 0.5540 & 0.6255 & 0.0382 & 0.9552 \\ \hline Adam & 0.8868 & 0.2560 & 0.6231 & 0.5789 & 0.0443 & 0.9521 \\ \hline RMSProp & 0.8733 & 0.2673 & 0.5906 & 0.6000 & 0.0523 & 0.9445 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Average Precision and Log-average Miss Rate for three optimizers and three threshold values. Figure 5: Comparison of the precision-recall curve (left side) and Log-average Miss Rate (right side) for the different optimizers and threshold values for the transverse crack YOLOv2 detector: (**a**,**b**) \(t=0.25\); (**c**,**d**) \(t=0.50\); (**e**,**f**) \(t=0.75\). Figure 6: _Cont._  
The Adam optimizer provides a lower amount of multiple detections in one object and the ability to detect more relevant objects within an image than SGDM and RMSProp. Even though, in some cases, a higher confidence score was obtained from SGDM and RMSProp, the Adam optimizer was chosen for further work. The proposed model based on YOLOv2 is focused on detecting one class object (transverse crack); thus, the \(mAP\) has the same value as \(AP\) (Equation (13)). The Precision, Recall, and \(F1\) Score obtained from the YOLOv2 model are shown in Table 3.  
[PERSON] et al. [16] considered an \(IoU\) over 50% (\(t=0.50\)) between the predicted box and the ground truth box as a successful match. The transverse road crack was represented by the class name D10. Precision reached a value of 0.7818, Recall of 0.6571, and an \(F1\) Score of 0.7137, which is a better result than in this study, when Precision ranged between 0.5540 and 0.6231 (Table 2) for threshold value \(t=0.50\). Precision for all distress types reached a value of 0.8852 and Recall a value of 0.8710. [PERSON] et al. [17] determined that if a prediction box captured over 30% (\(t=0.30\)) overlap with the ground truth box, the prediction was considered a successful match. The value of Precision was 0.90, Recall was 0.83, and the \(F1\) Score was 0.86 for the transverse crack class, which is quite similar to the values in this study. [PERSON] et al. and [PERSON] et al. [17; 16] used the YOLOv2 framework for automatic road distress detection, whereas [PERSON] et al. [18] utilized the YOLOv5 detector in four different variants. For an \(IoU\) greater than 50% (\(t=0.50\)), Precision between 0.851 and 0.881 was achieved for the road cracks class. Precision for all detected classes reached values between 0.891 and 0.908, and Recall reached values between 0.512 and 0.582. It can be stated that [PERSON] et al.