## 5 Critical Analysis
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline
**Ref** & **Method** & **Backbone** & **Framework** & **Dataset** & **Surface** & **Loss Function** & **Optimizer** & **Annotation Tool** & **Performance** \\ \hline
[65] & YOLOv5s & - & - & Own collection & Pavement & - & - & CVAT & F1-Score = 86.79\% \\ \hline
[66] & YOLOv5 & - & PyTorch & RDD & Asphalt & GloU and pavement & SGD & - & F1 = 67.39\% \\ \hline
[67] & CCapFFN & - & - & PCD19, CFD, Crack500, CrackTree260, CrackW100, Crack315 and Stone31 & - & - & - & Presition = 91.21\% Recall = 90.44\% F1-Score = 90.82\% \\ \hline
[68] & Faster R-CNN & ResNet & PyTorch 1.8 & CRACK500 & Pavement & Cross-Entropy & SGD & Manually & - \\ \hline
[15] & GCN & - & - & Own collection & Pavement & - & - & - & Precision = 84\% Recall = 75.5\% F1-Score = 77.2\% \\ \hline
[69] & Segmentation R-CNN & - & TensorFlow & Own collection & Asphalt pavements & Cross-Entropy & SGD & Manually & IoUs = 87.6\% \\ \hline
[70] & Mask R-CNN & ResNet-101 & Keras & Own collection & Asphalt pavements & - & Adam & Manually & Average accuracy = 92.10\% \\ \hline
[71] & DDSNet & - & TensorFlow & Own collection & Pavement & - & Adam & LabelImg &
\begin{tabular}{c} mAP = 89.25\% F-score = 96.18\% FPS = 16.74\% \\ \end{tabular} \\ \hline
[72] & CrackCLF & U-Net & PyTorch & \
\begin{tabular}{c} CFD, Crack500 and Crack700 \\ \end{tabular} & Pavement & - & Adam & Manually &
\begin{tabular}{c} Precision = 94.51\% F-score = 94.4\% F1-Score = 94.06\% \\ \end{tabular} \\ \hline
[73] & SegDecNet++ & U-Net & PyTorch & \
\begin{tabular}{c} CFD, Crack500, CrackTree200, \\ DeepCrack, \\ GAP384, \\ Nonsiblider and \\ Non-crack \\ \end{tabular} & Concrete & Cross-Entropy & Adam & Manually &
\begin{tabular}{c} Dice score = 81\% IoU = 71\% \\ \end{tabular} \\ \hline
[74] & CrackNet-M & - & TensorFlow & Own collection & \
\begin{tabular}{c} 3d asphalt \\ pavement \\ \end{tabular} & Focal & Adam & Manually &
\begin{tabular}{c} Precision = 94.28\%, \\ Recall = 93.89\%, \\ F-measure = 94.04\% \\ \end{tabular} \\ \hline
[75] & SROCD & - & - & \
\begin{tabular}{c} Shadow-Crack, \\ Crack500, CFD \\ and AEL \\ \end{tabular} & Pavement & Cross-Entropy & - & Labelme &
\begin{tabular}{c} AU = 0.514 \\ ODS = 0.783 \\ OIS = 0.846 \\ \end{tabular} \\ \hline
[76] & RCD-Net & - & TensorFlow & Crack500 & Pavement & Focal & SGD & - &
\begin{tabular}{c} Accuracy = 96.29\% Dice Coefficient = \\ 97.33\% \\ \end{tabular} \\ \hline
[77] & PCM-HED & - & - & \
\begin{tabular}{c} LS-3D, LCMS, \\ ESAR and 2D-1 \\ \end{tabular} & 3d pavement & Focal & - & - &
\begin{tabular}{c} Recall = 0.91\% F-values = 0.89\% F-values = 0.89\% \\ \end{tabular} \\ \hline
[78] & YOLOv5-CBoT & - & PyTorch & RDD & Pavement & - & SGD & - &
\begin{tabular}{c} Precision = 64.1\% F-values = 59.3\% F-score = 61.6\% F-score = 63.7\% \\ \end{tabular} \\ \hline
[79] & YOLOv4-Tiny & - & - & DeepCrack &
\begin{tabular}{c} Asphalt and \\ concrete \\ pavement \\ \end{tabular} & - & Adam & LabelImg & mAP = 54.88\% F-score = 94.54\% F-score = 97.16\% \\ \hline
[80] & YOLOv & \begin{tabular}{c} Darknet19 \\ ResNet50 \\ \end{tabular} & - & Own collection & \
\begin{tabular}{c} Tiled \\ sidewalks \\ \end{tabular} & - &
\begin{tabular}{c} SGD \\ Adam \\ \end{tabular} & - & Accuracy = 97.16\% F-score = 97.16\% \\ \hline \hline \end{tabular}  
\end{table} 
Table 14: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ref** & **Method** & **Backbone** & **Framework** & **Dataset** & **Surface** & **Loss Function** & **Optimizer** & \begin{tabular}{c} **Annotation** \\ **Tool** \\ \end{tabular}  & **Performance** \\ \hline
[48] & ConnCrack & DenseNet121 & - & \begin{tabular}{c} ImageNet, CFD \\ EdmCrack600 \\ \end{tabular}  & \ \begin{tabular}{c} Asphalt \\ pavement \\ \end{tabular}  & cWGAN & - & - & - & \begin{tabular}{c} Precision = 96.79\%; \\ Recall = 87.75\% \\ F1 Score = 91.96\% \\ \end{tabular}