# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 5 Conclusions  
Deep architectures, just as they have done in other areas, have improved the performance of crack detection approaches significantly. In this study, 61 deep learning-based crack detection approaches were comprehensively reviewed. More precisely, the crack detection approaches that use deep layers to perform the feature extraction step in an automated manner were reviewed and the main contributions of each study were extracted and discussed in detail. Moreover, additional details of several studies that employed the same public data sets have been provided to give the readers insights into the selection of the most promising crack segmentation approaches. Based on the number of studies in each category, the SS setting was chosen to focus on, as the most popular setting in the crack detection area in recent years. In addition to providing higher accuracy in terms of detection, the crack localization is done at the pixel-level thereby providing higher localization accuracy. By and large, the crack segmentation approaches proposed in the crack detection area are inspired by the proposed approaches in the SS field of the computer vision area. Even though promising approaches have been proposed so far, notable future directions can be proposed in the crack segmentation area. Identification of different types of cracks and their associated cause or causes is a vital phase in the selection of rehabilitation treatment. There has been a very strong concentration on crack detection using a SS setting. However, no study performed crack type classification with a SS setting. It is worth mentioning that the crack type classification has been considered in both IC and OR settings and promising performances have been obtained. Moreover, in addition to the considered crack types studied so far, there are other crack types, such as meandering and crescent-shaped cracks, which have not been considered. If these two types of cracks are detected and classified correctly, useful information about the base and asphalt layers can be achieved. According to the details provided in Section 4, it is imperative to develop a universal public crack data set in the area of crack detection. Although CFD and Aigle-RN data sets have been widely considered in the literature, they suffer from consisting of a limited number of images. This has resulted in state-of-the-art approaches not being compared fairly and properly. The universal public data set should be acquired in different illumination conditions (i.e., different times of a day) so that the effectiveness of the proposed approaches by researchers in different conditions can be validated. Additionally, different kinds of noise (both similar and non-similar to cracks), such as zebra crossings, oil spots, sealed cracks, leaves, and other objects on the road, etc., should be considered. More importantly, the universal public data set needs to be released with a specified number of training, cross-validation, and testing data. This will promote a trend of validation of the proposed crack segmentation approaches on the same data set so that all of the proposed approaches can be compared directly. Even though the main idea of crack segmentation approaches is drawn from the SS context in the computer vision area, surprisingly, there have been a few attempts to perform the task of crack segmentation in a weakly supervised or unsupervised manner. On the other hand, promising SS approaches with weakly supervised and unsupervised settings can be found in the computer vision area. It is well-known that deep architectures are data-hungry, and in a supervised mode, they require lots of annotated data to be trained with. In the case of the SS setting, data annotation at the pixel-level is considered a labour-intensive and time-consuming task. Therefore, performing crack segmentation task in a non-supervised manner will limit human intervention and make the automatic crack detection area one step closer to full automation.