# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 3 Results
### Buildings Crack Detection
#### 3.2.1 Binary Classification  
Given the specificity of the problem, the binary classification approach is well-suited for crack identification, distinguishing between two classes: cracks and no cracks. For this purpose, [PERSON] et al. [49] introduced an automated deep crack segmentation approach using variants of the U-Net deep learning model to achieve pixel-level crack segmentation in images of historic buildings. U-Net is a convolutional encoder-decoder network designed for end-to-end semantic segmentation tasks with limited amounts of data. Three U-Net architectures, including Deep ResU-Net, ResU-Net++, and U\({}^{2}\)-Net, were explored. U\({}^{2}\)-Net demonstrated superior performance, with an average Intersection over Union (mIoU) value of 78.38% due to its ability to effectively capture small and superficial cracks, including those located on image edges or in images with varying lighting and focus conditions. With the aim of accurately detecting cracks in historic masonry structures, [PERSON] et al. [50] developed an innovative segmentation model called CAM-K-SEG that integrates Grad-CAM visualization and K-Mean clustering with pre-trained convolutional neural network models. By comparing the performance metrics of several pre-trained CNN models, including VGG16, VGG19, Inception-V3, Xception, ResNet50, and a customized CNN model, the ResNet50 model was selected. A dataset of high-resolution photographs acquired from smartphones was used for training, and the performance of the proposed method was evaluated through a comparative analysis with the U-Net segmentation model. Evaluating the results according to the IoU metric, with an average value of 70%, the CAM-K-SEG model demonstrated superior ability in object identification and crack localization, whereas the U-Net model was more proficient in crack area segmentation. Differently, [PERSON] and [PERSON] [51] concentrated on automatic crack detection in historic concrete buildings. They developed a novel DL architecture called ReCRNet (Residual CRack Detection Network), which was applied to close-range images acquired from drones. The performance was compared with traditional ML-based classification methods (PCA + Linear SVM, PCA + DecisionTree) and DL-based classification methods (AlexNet, VGG19), showing superior performance in terms of accuracy, precision, recall, and F1-score. In addition, the proposed model leverages residual learning, resulting in a lightweight architecture designed for fast and high-precision classification and suitable for personal computers and small datasets. Unlike standard residual networks, such as ResNet, which are too large for  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Reference** & \begin{tabular}{l} **DL Algorithm** \\ **(Architecture)** \\ \end{tabular} & \begin{tabular}{l} **Accuracy** \\ **mPA [\%]** \\ \end{tabular} & \begin{tabular}{l} **Precision** \\ **mAP [\%]** \\ \end{tabular} & \begin{tabular}{l} **Recall** \\ **[\%]** \\ \end{tabular} & \
\begin{tabular}{l} **F1-Score** \\ **[\%]** \\ \end{tabular} &
\begin{tabular}{l} **IoU/mIoU** \\ **[\%]** \\ \end{tabular} \\ \hline \multicolumn{6}{l}{BINARY CLASSIFICATION} \\ \hline
[49] &
\begin{tabular}{l} CNN \\ (U\({}^{2}\)-Net) \\ \end{tabular} & 98.32 & 67.73 & 83.03 & & 78.38 \\ \hline
[50] &
\begin{tabular}{l} CNN CAM-K-SEG \\ (ResNet50) \\ \end{tabular} & 95.44 & 97 & 100 & & 70 \\ \hline
[51] &
\begin{tabular}{l} CNN \\ (ReCRNet) \\ \end{tabular} & 97.10 & 100 & 93.30 & 96.60 & \\ \hline \multicolumn{6}{l}{EDGES EXTRACTION} \\ \hline
[52] &
\begin{tabular}{l} DexiNed (Xception) \\ RCF (VGG16) \\ \end{tabular} & 88.83–90.62 & 47.14–63.39 & 49.58–60.82 & 50.15–61.38 & 33.47–44.28 \\
3.2.81–44.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Review of image-based DL models applied for buildings crack detection on HBE, with accuracy values obtained.  
binary classification tasks, this model was optimized for efficiency and performance on limited training samples.