# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Object Recognition
#### 2.2.3 YOLO Family  
The YOLO name stems from the fact the YOLO architectures need to look at the image once and perform the task of OR in one forward propagation. YOLO [48], YOLO9000 [60], and YOLOv3 [61] are members in this family. YOLOv3 is able to perform the OR task with accuracy comparable to other methods with a notably faster processing time. To the best of the authors' knowledge, as the main architecture from the YOLO family, YOLO9000 has been employed in the crack detection area only once. [PERSON] et al. [62] utilised YOLO9000 to perform crack detection in a diverse data set that included different types of cracks and defects.  
The reason OR failed to become the most popular setting after the IC setting (Figure 1) is the undesirable performance in terms of crack localization in this setting as well. The OR setting also results in a coarse crack localization output. Moreover, in this setting, the overlapping of bounding boxes when the algorithm fails to capture the whole crack as a single object is problematic. A summary of studies of deep crack detection approaches based on object the recognition setting is shown in Table 2.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref** & **Novelty/Novelties** & **Core Architecture** \\ \hline
[3] & 1. First application of the OR setting for the task of defect detection. 2. Considering a richer data set, including five types of defects in comparison to the previous study. & Faster R-CNN \\ \hline
[57] & 1. Collecting and making publicly available a new data set including eight types of road damage. 2. Applying the SSD framework with two different backbone architectures for the sake of comparison. & SSD \\ \hline
[53] & 1. Proposing a new RPN dealing with different scales and improving accuracy. 2. Considering defect localization in the map using the NetVLAD module. & Faster R-CNN \\ \hline
[56] & 1. Considering 14 defect types in the data set. 2. Performing a comparative study between OR and SS settings. & Faster R-CNN \\ \hline
[62] & Application of YOLO9000 architecture for crack detection of roads in an OR setting. & YOLO v2 \\ \hline
[52] & 1. Application of R-CNN framework to perform crack detection in an OR setting. 2. Employing a pre-trained CNN on ImageNet and Cifar-10 data sets to perform crack classification and bounding box regression. & R-CNN \\ \hline
[54] & 1. Proposing CrackDN consisting of Faster R-CNN, a sensitivity detection network, and a region proposal refinement network. 2. Considering a richer data set where different backgrounds of normal, unbalanced illuminations, with markings and shadings, are included. & Faster R-CNN \\ \hline
[55] & Considering complex backgrounds in the images including handwritten scripts and applying Faster R-CNN to identify cracks in the images. & Faster R-CNN \\ \hline
[4] & Combining Faster R-CNN with a new Bayesian integration algorithm to suppress false detection and address challenging vision problems where a simple end-to-end learning strategy might not be effective. & Faster R-CNN \\ \hline \end{tabular}
\end{table}
Table 2: Summary of deep crack detection approaches based on the OR setting.