# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 2 State-of-the-Art Studies for Crack Segmentation
Moreover, the impact of various DL architectures, regularization techniques, network depths, and transfer learning methods was examined. The proposed approach achieved accuracy and an F1 score of 99.5% and 100%, respectively. In general, based on the surveyed related literature, several aspects related to crack detection have been addressed. However, there is a very limited number of studies addressing the problem of pixel-level crack detection in the field of historical heritage. Consequently, the approach proposed in this paper investigates the performance of adopting pixel-level deep crack segmentation models on historical surfaces using variants of the U-Net deep learning model in order to address segmentation problems with limited amounts of data. Table 1 summarizes the presented exhaustive survey of state-of-the-art studies related to crack segmentation based on deep learning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Method** & **Feature** & **Post Processing** & **Datasets** & **Performance** \\ \hline
[11] & U-Net and FPN with different CNN as encoder & CNN learned features & N/A & Dataset of 351 images containing cracks and 118 intact images & F1 score = 79.6\% \\ \hline
[7] & An end-to-end deep hierarchical CNN with a special loss function & Multi-scale and multi-level CNN features & CRFs and GF methods & Dataset of 337 images with manually annotated maps & mIoU = 85.9\%, J annotated maps & Best F1 score = 86.5\% \\ \hline
[12] & An end-to-end trainable deep CNN with a multi-scale dilated convolution CNN features & Multi-scale dilated convolution CNN features & N/A &
\begin{tabular}{c} Datasets of total 4736, 1030, and 2416 crack images are used as training, validation, and testing set \\ CFD and AlgebraR datasets used for & Precision = 80.0\%, \\ mIoU = 73.53\% \\ \hline
[13] & Context-aware deep CNN & CNN learned features & N/A &
\begin{tabular}{c} CrackForest Dataset (CFD), \\ Monitoring Management Dataset \\ (TRIMOD, Customized Field Test \\ Dataset (CFTD)) \\ \end{tabular} & F1 score = 0.8234, 0.7937, 0.8252 \\ \hline
[15] &
\begin{tabular}{c} A U-Net with a pre-trained \\ ResNet-34 as an encoder, followed by \\ SCSF modules in the decoder \\ \end{tabular} & CNN learned features & N/A & CFD dataset, Crack500 dataset & F1 score = 96\%, 73\% \\ \hline
[16] & \
\begin{tabular}{c} An end-to-end deep FCN with pre-trained VGG16 as the encoder part & CNN learned features & N/A &
\begin{tabular}{c} Crack dataset, containing 40,000 \\ images \\ \end{tabular} & Average precision = 90\% \\ \hline
[18] & DeepLabv3+ & Multi-scale CNN features & \begin{tabular}{c} Skeletonizing and FPT \\ methods \\ \end{tabular} & \
\begin{tabular}{c} Dataset of 300 crack images captured \\ using a smartphone, another dataset of \\ 80 pavement crack images \\ \end{tabular} &
\begin{tabular}{c} mIoU = 0.8342, 0.7331 using \\ validation and testing \\ datasets \\ \end{tabular} \\ \hline
[10] & \
\begin{tabular}{c} An encoder-decoder based on a \\ modified SegNet architecture \\ \end{tabular} & CNN learned features & N/A &
\begin{tabular}{c} Datasets of 2000 bridge index images \\ and 1000 parameter crack images \\ \end{tabular} & Accuracy = 90\% \\ \hline
[19] & A crack segmentation method based on DLPP & DLPP features & N/A &
\begin{tabular}{c} 326,000 images collected from 45 \\ various bridges \\ \end{tabular} & The proposed method was effective and robust \\ \hline
[14] & \
\begin{tabular}{c} A hybrid method based on a Faster \\ R-CNN, modified \\ TuFF and DTM algorithms \\ \end{tabular} & Level set function & N/A &
\begin{tabular}{c} A dataset of 100 images captured from \\ various places \\ \end{tabular} & mIoU = 83\% \\ \hline
[20] & \begin{tabular}{c} Source crack detection approach based \\ on a much improved VOC-16 transfer \\ learned model \\ \end{tabular} & CNN learned features & N/A & \
\begin{tabular}{c} SDNET2018 dataset \\ \end{tabular} &
\begin{tabular}{c} Accuracy = 99.5\% and F1 \\ score = 100\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}