# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Object Recognition  
OR is defined as a collection of related tasks to identify objects of interest in images using bounding boxes [46]. In the computer vision area, its application has improved the accuracy of object detection and localization in the images compared with the application of the sliding window technique for the same purpose [47]. In the crack detection area, the output of this framework is the input images where the crack areas are detected using bounding boxes as can be seen in Figure 5.  
In the area of computer vision, there are several families of architectures that are used to perform OR, such as region-based convolutional neural networks (R-CNN) [47], you only look once (YOLO) [48], and single shot detector (SSD) [49]. Members of the R-CNN family have been most used in the area of crack detection. To the best of the authors' knowledge, SSD and YOLO architectures are only used once to perform crack \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref** & **Novelty/Novelies** & **Core Architecture** \\ \hline \multirow{2}{*}{[24]} & The first application of deep learning for crack classification using an IC setting. & - \\ \hline \multirow{2}{*}{[23]} & Application of a sliding window technique resulting in scanning images with any resolution at the testing & \multirow{2}{*}{MatConvNet} \\  & phase. & \\ \hline \multirow{2}{*}{[25]} & Investigation of the effect of the number of layers on the performance. & - \\ \hline \multirow{2}{*}{[31]} & 1. Investigation of the effect of patch size on the performance. 2. Crack type classification using PCA. & - \\ \hline \multirow{2}{*}{[30]} & 1. Crack type classification. 2. Applying active learning in the training phase. & ResNet \\ \hline \multirow{2}{*}{[37]} & 1. Application of the pre-trained VGG-16 on ImageNet data for feature extraction. 2. Application of classical & \multirow{2}{*}{VGG-16} \\  & machine learning algorithms to classify the extracted features. & \\ \hline \multirow{2}{*}{[44]} & Utilization of a larger data set with complex and diverse pavement surfaces (3D images). & - \\ \hline \multirow{2}{*}{[45]} & 1. Application of a shallow network (inspired by LeNet) and ANIVOS architecture (inspired by VGG and AlexNet) to perform deep crack detection. 2. Collecting and making GAPs data set publicly available. & LeNet, AlexNet, VGG-models \\ \hline \multirow{2}{*}{[27]} & IC of road images into crack and non-crack images using deep learning and comparing the performance & - \\  & with handcrafted features-based approaches. & \\ \hline \multirow{2}{*}{[41]} & Performing a comparison between the application of AlexNet for crack detection in two settings of (i) & AlexNet \\  & training from scratch and (ii) transfer learning. & \\ \hline \multirow{2}{*}{[28]} & Performing a comparative study between the application of deep architectures relying on transfer learning and different edge detectors to perform crack detection. & AlexNet \\ \hline \multirow{2}{*}{[43]} & In addition to crack detection, performing crack geo-localization using ultrasonic beacon system to cope & \multirow{2}{*}{MatConvNet} \\  & with the limitations of GPS. & \\ \hline \multirow{2}{*}{[39]} & Performing crack detection in an IC setting using CNN and relying on transfer learning. & VGG-16 \\ \hline \multirow{2}{*}{[40]} & 1. Application of pre-trained AlexNet on ImageNet data to perform crack detection. 2. Considering a richer & AlexNet \\  & data set where non-crack objects are included. & \\ \hline \multirow{2}{*}{[29]} & Performing a comparison between two frameworks of crack detection using (i) fully connected CNN and (ii) & AlexNet \\  & based on speeded-up robust features method. & \\ \hline \multirow{2}{*}{[32]} & Focusing on crack detection in black-box images and classifying images into the crack, road marking, and & - \\ \hline \multirow{2}{*}{[26]} & 1. Investigation of the effect of receptive field size on the performance. 2. Multi-class classification of & Inspired by AlexNet \\  & different types of cracks. & \\ \hline \hline \end{tabular}
\end{table}