## 3 Results
### Studies Using Image Databases (IDS)
#### 3.3.2 Type of Classification  
Machine learning and deep learning approaches allow classification problems with different complexity to be addressed. In the field of SHM, this can lead to an architecture capable of detecting the presence or absence of a specific condition (i.e., binary classification), such as distinguishing structurally damaged areas from intact sections. Alternatively, they can be used for more specific classification purposes based on recognizing different categories of structural deficits (multi-class classification), identifying, for example, the type of damage, type of materials, shape of cracks, and other specific features. Table 3 provides a summary of the main types of classification addressed by studies using image datasets. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Author (year)** & **Method** & \begin{tabular}{c} **Binary** \\ **Classification** \\ \end{tabular} & \
\begin{tabular}{c} **Multi-Class** \\ **Classification** \\ \end{tabular} &
\begin{tabular}{c} **Transfer** \\ **Learning** \\ \end{tabular} \\ \hline [PERSON] et al. [86] & Deep Learning & X & X & \\ \hline [PERSON] et al. [87] & Deep Learning & X & X & \\ \hline [PERSON] et al. [88] & Deep Learning & X & & \\ \hline [PERSON] et al. [89] & Deep Learning & X & & \\ \hline [PERSON] et al. [92] & Deep Learning & X & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification characteristics. Regarding deep learning approaches, most of the selected studies addressed only binary classification problems, including [88, 89, 92, 93, 99, 100, 102, 105, 110]. Only a few studies also addressed multi-class classification problems in addition to binary classification, such as [86] (type of damage: four classes), [96] (bridge components: seven classes), [87] (several multi-class subsets), [104] (type of damage: three classes), [107] (several multi-class subsets), and [113] (type of damage: five classes). The authors of [105] also mentioned labeling for the multi-class classification of cracks based on their size (in mm), but they did not present the results of this task. Pre-trained networks have also been combined with transfer learning processes to solve two common issues in DL: insufficient data to train networks from scratch and the reusability of pre-trained models to tackle similar tasks. This approach was used, for example, in [107], in which the authors used pre-trained neural networks to detect structural damage on images from other sources. Moreover, in [110], the authors investigated the potentiality of transfer learning to classify cracks on other materials and targets (cracks in pavement, concrete materials, and brickwork materials). The same occurred in [113], where the authors applied pre-trained models on a small set of images (only seven) taken from different infrastructures. In addition, some studies have also applied deep learning models for purposes other than classification. For example, [92] proposed a method to estimate damage measurements using the neural network classification results (areas with damage). In [95], pre-trained models were used to estimate displacements (and vibrations) in structures at the sub-pixel level. In contrast, only a few studies have adopted machine learning approaches based on supervised classifiers, which are less demanding in terms of computation time and architectural complexity. Among them, the authors in [101] implemented a cubic SVM classifier to identify cracked areas on concrete surfaces (binary classification) and to determine the characteristics (width and length) of single and multi-branch cracks on the recognized areas of damage. In [106], a machine learning approach with an SVM classifier was used to detect areas of pitting corrosion. In [111], the AdaBoost algorithm [112] was used to discriminate between areas with and without cracks.