# Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
## 6 Conclusions  
In the present study, a new idea of embedding deformable modules into traditional region-based crack detectors was proposed. The new crack detector not only improves the precision of crack detection, but also enables the detection of out-of-plane cracks that are difficult for traditional crack detectors to detect. Three networks, including the Faster R-CNN, R-FCN, and FPN-based Faster R-CNN, were adopted to demonstrate the feasibility and efficacy of the new crack detector. A total 1860 images, with uniform size of \(227\times 227\) pixels, of three types of cracks (alligator, longitudinal, and transverse) were obtained and used as the dataset. Thirty-six cases were investigated by using the three detectors with and without deformable modules. The testing results show that with the addition of deformable modules the mAPs of the Faster R-CNN, R-FCN, and FPN-based Faster R-CNN were improved by 1.76%, 2.44%, and 4.41%, respectively. Specifically, under the optimal configuration, the mAPs of the three detectors with deformable modules were increased by 3.98%, 4.28%, and 4.81%, respectively, compared to that of the three detectors without deformable modules. More importantly, embedding deformable modules enables the three detectors to detect out-of-plane cracks in concrete. Due to  
Figure 15: Comparison of the performance of the six detectors on five new images. (**A–C**) were detected by Faster R-CNN, R-FCN, and FPN-based Faster R-CNN without deformable modules, respectively; (**D–F**) were detected by Faster R-CNN, R-FCN, and FPN-based Faster R-CNN with deformable modules, respectively.  
the multi-scale feature extraction of FPN, the FPN-based Faster R-CNN showed the best performance among the three detectors, achieving the highest mAP of 92.11% and APs of 100%, 89.41%, and 86.93% for alligator, longitudinal, and transverse cracks, respectively.  
Although deformable modules have proven to improve the detection accuracy of in-plane and out-of-plane cracks, at this stage it only works for surface-breaking cracks and still has difficulty to detect crack depth. In the future, efforts will be made to use 3D image reconstruction technology to further improve the detection accuracy of in-plane and out-of-plane cracks, as well as the performance on crack depth detection. In addition, the influence of where the deformable module is embedded on the performance of the deformable convolutional networks will be further studied in future research work.  
Conceptualization, L.D. and W.W.; methodology, H.-H.C. and P.S.; investigation and result analysis, H.-H.C., P.S., and X.K.; writing--original draft, H.-H.C., W.W.; supervision and writing--review and editing, L.D.; project administration, L.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant numbers 51808209, 51778222) and Natural Science Foundation of Hunan Province (2019J50065). The authors gratefully acknowledge the reviewers for their useful suggestions and comments. The authors declare no conflict of interest with respect to the research, authorship, and publication of this article.