## 1 Introduction
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Paper** & \begin{tabular}{c} **Feature** \\ **Enhancement/** \\ **Pre-Processing** \\ \end{tabular} & \begin{tabular}{c} **Classification of** \\ **Fracture** \\ **Dimensions** \\ \end{tabular} & \begin{tabular}{c} **Type of Neural** \\ **Network** \\ \end{tabular} & **Open-Source** & \begin{tabular}{c} **Image** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Filter** \\ **Size** \\ \end{tabular} & \
\begin{tabular}{c} **Filter** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Images in** \\ **the Dataset** \\ \end{tabular} & **Accuracy, \%** \\ \hline
[24] & N & N & CNN & VGG16 & 256 \(\times\) 256 & 5 \(\times\) 5 & 3500 & 92.3 \\ \hline
[8] & Y & Y & CNN & N/A & 227 \(\times\) 227 & & 40,000 & 99.9 \\ \hline
[14] & Y & Y & \
\begin{tabular}{c} ANN vs. \\ Fuzzy \\ \end{tabular} & N/A & 256 \(\times\) 256 &
\begin{tabular}{c} Object \\ Detection \\ \end{tabular} & 205 & 96.1 \\ \hline
[16] & & & &
\begin{tabular}{c} CNN vs. \\ SVM vs. \\ Boost \\ \end{tabular} & ConvNet & 227 \(\times\) 227 & & 40,000 & 99.7 \\ \hline
[9] & Y & & CNN & N/A & 256 \(\times\) 256 & & 40,000 & 98 \\ \hline
[15] & Y & \
\begin{tabular}{c} Image \\ Segmentation \\ Approach \\ \end{tabular} & No & N/A & &
\begin{tabular}{c} Automatic \\ Peak \\ Detection \\ \end{tabular} & N/A & N/A \\ \hline
[19] & N & N & N &
\begin{tabular}{c} KNN vs. \\ Desion Tree vs. \\ SVM vs. \\ Ranorest \\ \end{tabular} & 227 \(\times\) 227 & & 20,000 & \\ \hline
[4] & Y & Y &
\begin{tabular}{c} No (Review \\ Paper) \\ \end{tabular} & N/A & N/A & N/A & N/A & N/A & N/A \\ \hline
[20] & Y & N & CNN &
\begin{tabular}{c} AlexNet, \\ LeNet-5, \\ CIFAR10 \\ \end{tabular} & \(1024\times 1024\) & \(32\times 32\) & & 94 \\ \hline
[21] & Y & N & CNN & N/A & N/A & N/A & ASPP & N/A & 96.7 \\ \hline
[23] & N/A & N/A & CNN-LSTM & N/A & N/A & N/A & N/A & N/A & \\ \hline
[22] & N/A & N/A & N/A &
\begin{tabular}{c} New CNN vs. \\ R-CNN vs. \\ U-Net \\ \end{tabular} & N/A & \(576\times 576\) & N/A & N/A & N/A & 82.5 \\ \hline
[17] & N & N & FCN & VGG16 & 227 \(\times\) 227 & & 40,000 & 90 \\ \hline
[18] & N/A & N/A & N/A & CNN & AlexNet & \(3120\times 4160\) & N/A & 60,000 & 99.06 \\ \hline
[13] & Y & N &
\begin{tabular}{c} Backpropagation \\ NN \\ \end{tabular} & N/A & N/A & N/A & N/A & 225 & 92 \\ \hline
[25] & N & Y & R-CNN & & & & 376 & \\ \hline
[11] & N/A & N & U-Net & N/A & N/A & N/A & N/A & N/A & 78.12 \\ \hline
[26] & & & \
\begin{tabular}{c} AlexNet, \\ VGGNet13, \\ and ResNet18 \\ \end{tabular} & &
\begin{tabular}{c} 227 \(\times\) 227 \\ \end{tabular} & & & 10,000 & \\ \hline
[27] & N & N & CNN & N/A & \(16\times 16\) & N/A & N/A & 64,000 & 93 \\ \hline
[28] & Y & N & SVM & N/A & N/A & N/A & N/A & N/A & 94 \\ \hline
[29] & N & N & ANN & N/A & N/A & N/A & N/A & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The overview of image processing methods and application of neural networks for crack identification in concrete structures. Thermal image-based crack identification and detection is performed for the first time using a U-Net type learning network in [11]. The prediction accuracy of U-Net using thermal images is rather low--approximately 78%. In [26], the authors show a comparison of three neural networks, namely, AlexNet [30], VGGNet13 and ResNet18 for the detection and classification of crack images. The tests indicate that the ResNet18 model generates the most satisfactory results. Moreover, it is shown that the trained YOLOv3 model detects the crack area with a satisfactory accuracy. Complete turn-key solutions and frameworks for crack detection from images of various scenes have been developed recently. One such solution is DeepCrack, an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation [31; 32].