## 5 Critical Analysis
### Classification  
[PERSON] et al. [14]. developed the deep convolutional neural network (DCNN) algorithm based on the VGG-16 backbone. The Federal Highway Administration (FHWA) and Long-Term Pavement Performance (LTPP) datasets were used to train the DCNN algorithm. It was also applied to hot mix asphalt (HMA) and Portland cement concrete (PCC) pavement images, and the highest accuracy of up to 90% was finally achieved using the Adam optimizer. [PERSON] et al. [50]. proposed an unsupervised learning method for road crack classification. The method fuses a k-means clustering algorithm with a CNN, and the backbone of the method is AlexNet. The author created his own dataset using smartphones and digital cameras, and compared the method with the traditional method for classification experiments. The test results showed that the method has higher accuracy. [PERSON] et al. [51]. investigated the feasibility of using Google Street View (GSV) to assess the quality of roads. The development of a CNN was specifically aimed at identifying road cracks in GSV. The author compared the results of the experiment with those of a commercial visual inspection company for road quality assessment. The results demonstrate the feasibility of intercepting GSV image datasets for road quality assessment. [PERSON] et al. [8]. investigated the combination of infrared imaging and deep learning for road crack detection. The team created a dataset containing three types of images: visible, infrared, and fusion (a combination of visible and infrared). Thirteen CNN models were tested and evaluated on this dataset. The test results show that infrared images have the highest accuracy and visible images have the lowest accuracy. The experiment demonstrates that the use of infrared imaging technology in road crack detection can significantly improve the detection accuracy. Another paper by [PERSON] et al. [52]. also combines infrared thermography and convolutional neural networks for crack detection in asphalt roads. The paper also categorized the images into visible, infrared, and fusion. The difference is that the experiment further tested the effectiveness of the image for transfer learning. The results of the experiment showed that the model trained from scratch performed best on the fusion image, while the transfer learning model performed well on the visible image. However, the experimental \begin{table}
\begin{tabular}{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
[98] & ECSNet & - & - & DeepCrack & Pavement & \
\begin{tabular}{c} BCE With \\ Logits \\ \end{tabular} & Adam & - &
\begin{tabular}{c} F1 score = 84.4\% \\ IoU = 73.08\% \\ FPS = 73.29 \\ \end{tabular} \\ \hline
[99] & STA & - & PyTorch & \begin{tabular}{c} CFD \\ Crack 500 \\ \end{tabular} & \
\begin{tabular}{c} Asphalt and \\ concrete \\ pavement \\ \end{tabular} & Dice & SGD & Manually &
\begin{tabular}{c} Precision = 84.08\% \\ F1 score = 87.08\% \\ Recall = 90.79\% \\ \end{tabular} \\ \hline
[100] & PSA-Net & ResNet-101 & PyTorch & Own collection & \begin{tabular}{c} Asphalt and \\ concrete \\ pavement \\ \end{tabular} & Dice & \
\begin{tabular}{c} Adam \\ SGD \\ \end{tabular} & - &
\begin{tabular}{c} Precision = 99.5\% \\ Recall = 94.50\% \\ Accuracy = 98.70\% \\ F1-score = 96.44\% \\ \end{tabular} \\ \hline
[101] & SegNet & - & PyTorch & \
\begin{tabular}{c} DeepCrack \\ CFD \\ Crack500 \\ \end{tabular} & Pavement & Focal & SGD & - &
\begin{tabular}{c} MIo = 87.53\% \\ Precision = 87.67\% \\ Recall = 85.41\% \\ F1-score = 86.52\% \\ \end{tabular} \\ \hline \hline \end{tabular}  
* \({}^{\prime}\)-\({}^{\prime}\) Denotes the paper did not provide the particular information.  
\end{table} 
Table 15: _Cont._results only performed well on less severe cracks and were less accurate on more severe crack images. [PERSON] et al. [9].