# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 4 Experimental Evaluation
### Two Possible Methods for Improved Accuracy  
The structure of Mask R-CNN used in this study is a modified version of the work of [PERSON] et al. [33], especially with regard to the mask branch. The COCO dataset for which the Mask R-CNN was developed contains 20 types of objects whose shapes are large, regular, and patterned, while the concrete damages studied in this paper are small, slender, irregular, and weakly patterned. For example, cracks are generally very thin and are a couple of pixels wide. Efflorescence can have either a radiated or downflow shape with unclear boundaries. Spalling may or may not contain exposed rebars, and their aggregate patterns may be strong or weak. Thus, the sizes of the network parts can be modified considering the characteristics of the damage for accurate localization and segmentation purposes.  
For accurate localization, the size of the RPN can be increased. RPN, which serves a practical detection role in Mask R-CNN, was originally designed to speed up the slow region proposal process of a selective search. To improve the speed of the region proposal, the network size of the RPN is designed to be relatively small. However, the inference of the trained Mask R-CNN should not only be sufficiently fast but also accurate. To achieve better performance in detection accuracy, a good alternative is to increase the resolution or depth of the RPN or add additional networks to Mask R-CNN. The feasibility of this approach was investigated in the work of [PERSON] and [PERSON] [42] and can be applied to further research.  
For accurate segmentation, the size of the mask branch can be further increased. The size of the mask branch is also relatively small compared to the backbone network ResNet-101 [38]. The mask branch originally had a low resolution because the performance of the mask branch with a 28 \(\times\) 28 resolution is accurate enough to estimate object shapes in the COCO dataset [34]. However, in the case  
Figure 8: Examples of collocated damage detection results (left column: original images with ground truth, right column: detection result of trained Mask R-CNN): (**a**) detection result of Collocated damages-5 in Table 1, (**b**) detection result of Collocated damages-10 in Table 1, (**c**) detection result of Collocated damages-7 in Table 1 and (**d**)detection result of Collocated damages-2 in Table 1.  
of cracks, spalling, and efflorescence, their shapes are too irregular to be estimated by a small network with a 28 \(\times\) 28 resolution. In this study, the resolution of the mask branch is set to 56 \(\times\) 56 in advance to avoid poor detection results. Although the resolution of the mask branch is enhanced, there are still a few detection results with inaccurate mask estimation. For instance, the efflorescence detection result in Figure 6b missed the slender parts of the efflorescence, and the crack detection result in Figure 8b also missed small parts of cracks where the crack width was relatively narrow. To improve the quality of mask estimation, the resolution of the mask branch can be increased even higher, but this approach may have limitations when it comes to the computational cost. Therefore, a good solution is to apply a statistical image-processing-based approach in addition to simply increasing the resolution of the mask branch to improve the accuracy of damage detection.