# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Image Classification  
In deep crack detection approaches based on the IC settings, the decision is limited to image/image patch-level and the trained architecture decides whether the new input is a crack contained one or not. Once the classification is made, the overall skeleton of the crack can be achieved by stacking the positive patches. A typical output of a crack detection approach based on the IC setting is shown in Figure 4. The overall architecture that is employed to perform IC consists of two parts. The first part is responsible for extracting meaningful features layer by layer from raw images, which is formed by successive convolutional and max-pooling layers. The second part consists of fully connected layers and is responsible for classifying the features extracted by the first part into certain classes [23]. To the best of the authors' knowledge, the first application of deep learning to perform crack detection with an IC setting was performed on 2017 [24], which investigated crack detection of concrete images where the image data set includes five different classes. The considered classes are cracked, chalk letter, joint, surface, and other. After this first application, researchers started investigating different aspects of the problem. [PERSON] et al. [25] studied the effect of depth (the number of deep layers) on the crack detection capability of the network. They concluded that the deeper the networks are, the more information can be learnt by the architecture. In [26], four architectures with different receptive fields were utilised and compared. Figure 3: Categorization of image-based crack detection approaches. Figure 2: Developing a deep learning-based crack detection system framework. The main research objective, in [27, 28], was a direct comparative study between crack detection approaches based on handcrafted features and deep architectures. The crack detection performance of different edge detectors, e.g., Sobel, Canny, Prewitt, Butterworth, etc., was compared with the application of deep architectures in an IC setting. [PERSON] et al. [29] performed a comparative study between the application of the \"speeded-up robust features\" method based on the Hessian matrix and Haar wavelet and automatic feature extraction using convolutional neural network (CNN) for the task of crack detection. Several studies have focused on looking at the problem from a multi-class classification point of view and considering crack type classification in addition to crack detection [26, 30, 31, 32]. [PERSON] and [PERSON] [31] investigated crack type classification using the principal component analysis (PCA) algorithm after crack detection using CNN. ResNet [33] architecture was employed by [PERSON] et al. [30] to detect and classify three types of defects on concrete surface images. In the case of road images, a multi-class classification approach based on CNN was proposed by [PERSON] et al. [32] resulting in classifying road areas into the crack, road marking, and intact areas. In [26], in addition to classification of crack type into five classes, inspired by AlexNet [34] and LeNet [35], four CNNs with different depths were considered and compared. It is well-known that the training of deep architectures requires a large amount of annotated data, making their application in different areas challenging. To solve this challenge, it has been proven that the application of pre-trained networks on large-scale annotated image data sets (e.g., ImageNet), which is known as transfer learning could be effective [36]. [PERSON] et al. [37] utilised the pre-trained VGG-16 [38] architecture on ImageNet data set to extract features and inputted them into different classical machine learning algorithms to perform crack detection. In [39, 40], the pre-trained VGG-16 [38] and AlexNet [34] on ImageNet data set, respectively, were employed to extract features. Then, the extracted features were fed to fully connected layers and a soft-max layer to perform the classification instead of classical machine learning algorithms.