# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Object Recognition
#### 2.2.1 R-CNN Family  
R-CNN [47], Fast R-CNN [50], and faster R-CNN [51] are members of this family, and faster R-CNN has been the most popular in the crack detection area. As the first member of the R-CNN family, R-CNN [47] architecture has been applied in the crack detection area only once [52]. In that study, the selective search was first used to detect candidate regions of cracks. Then, a CNN pre-trained on ImageNet and Cifar-10 data sets was utilised for crack classification and bounding box regression. To the best of the authors' knowledge, [PERSON] et al. [3] were the first to perform crack detection using an OR setting. That study is an improvement to their previous study where crack detection was carried out in an IC setting using a sliding window technique [23]. The improvements are two-fold. The first is inclusion of a richer data set and the second is employment of Faster R-CNN architecture and solve the crack detection challenge in an OR setting. After the first application, researchers considered different research objectives in this subcategory. In general, the research objectives can be categorised as (i) improving the Faster R-CNN performance via the application of extra modules and (ii) considering a richer data set that includes more complex images.  
[PERSON] et al. [53] designed a multi-scale defect region proposal network (RPN) that proposes candidate bounding boxes in different layers resulting in higher detection accuracy. They also performed crack geo-localization using an extra deep architecture and a database of geo-tagged images. It is noteworthy that both the crack detection network and the geo-localization module are incorporated in one network. An improved version of faster R-CNN in the crack detection area called CrackDN was proposed in [54]. In this framework, a pre-trained CNN is used to extract deep features and along with it, a sensitivity detection network is employed leading to a faster training procedure. In [4], the novelty is incorporation of two extra modules into the Faster R-CNN architecture to suppress false detection. The first module is a simple and compact deep CNN, which is responsible for crack orientation estimation of the identified crack patches. The second module is a Bayesian integration algorithm to exploit local consistency and reduce the uncertainties of detected crack patches.  
With a goal of making the data set more challenging in the OR setting, in [54; 55; 56], complex data sets were considered to check the performance of Faster R-CNN architecture in more realistic situations. [PERSON] et al. [54] included images with unbalanced illumination, cracks on marking, and shades. In contrast, [PERSON] et al. [55] considered a concrete crack data set where images with handwritten scripts are included in the data set as well. They concluded that the handwritten scripts can be considered as a strong noise in concrete  
Figure 5: Typical output of a deep learning-based crack detection approach based on the OR setting [3].  
images. A richer data set was collected by [PERSON] et al. [56]. To annotate the data for the task of OR, the regions of interest for each video frame containing the damaged part of the road were extracted. The authors managed to extract 14 different damage classes and perform crack detection on the collected data set using faster R-CNN architecture.