# Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
## 4 Discussions and Comparative Study of the Testing Results
### Analysis and Discussion of the Testing Results  
Based on the discussion above, it can be seen that for all the three types of cracks under consideration, the detection accuracy of all the three detectors with deformable modules is improved  
Figure 11: Training loss against epoch. Original framework without adding deformable modules: (**a–c**); framework combined with deformable modules: (**d–f**). (Note: Deformable modules conveniently represented as DCN in all figures).  
compared to that of the corresponding detectors without deformable modules. The accuracy improvement for different cracks is different, with 4% to 8% improvement for longitudinal and transverse cracks and less than 1% improvement for alligator cracks. The reason why the accuracy improvement for the alligator crack is not obvious is that the detection accuracy of all the detectors without deformable modules is already very high (almost 100%). There may be two explanations for the high detection accuracy of the alligator crack: (1) the characteristics of the longitudinal and transverse crack are quite different from that of the alligator crack; and (2) the features of alligator cracks account for a large part of the image, which makes the object annotation more obvious. In addition, it is found that the learning rate and anchor size are quite important hype-parameters for training the model, while the batch size has little effect on the detection accuracy.  
It can be seen from Tables 1-3 that the Faster R-CNN, the R-FCN, and the FPN-based Faster R-CNN achieved a relatively better performance in Case 6, Case 8, and Case 6, respectively, and the mAP of the three detectors with deformable modules is 91.58%, 90.01%, and 92.11%, respectively. Figure 12 shows the variation of mAPs with the number of epochs of the detectors in their optimal cases, from which it can be seen that the overall detection accuracy of the three detectors increases sharply in the early stage of training, and gradually become stable after 50 epochs. With deformable modules, all three original detectors achieved better detection accuracy and the FPN-based Faster R-CNN achieved the highest detection accuracy due to the idea of multiscale detection.  
The precision-recall (PR) curves of the detectors in their optimal cases are drawn in Figure 13, from which it can be seen that with the improvement of recall, the precision of training model can still be maintained at a high level, which is particularly significant for detecting cracks in civil structures.  
Figure 12: Mean average precisions (mAPs) of the detectors under their optimal configurations versus the number of epochs.  
Figure 13: PR curves of the models under their optimal configurations.  
The visualization of some testing results obtained from the optimal cases mentioned above are shown in Figure 14. During the visualization process, the threshold score of the anchor and the threshold value of non-maximum suppression were adopted as 0.8 and 0.3, respectively. It can be seen from Figure 14 that the cracks captured under different shooting environments were accurately detected although several missed detections occurred. It should be noted that the detection accuracy of the detectors for detecting the longitudinal and transverse cracks can be further improved by augmenting the database.