## 4 Discussion
As mentioned above, the patchwise-approach allows analysis of a varied-sized image by dividing it into patch units. Moreover, the separated two neural networks for the classification and segmentation provide the same advantage. It should be noted that this advantage has been emphasized in the different engineering fields [32; 33; 34]. However, due to the lack of objective records of crack images and their characteristics, this study only performed verification on one-sized images. For analyzing crack characteristics, this study proposed IP-based methods. Thinning and tracking algorithms was used for the crack length from the partitioned cracks by the CNN-based method, and the profiling algorithm is implemented for the crack width. Through the verification, this study showed a very small measure error rate of about 1%. This result indicates the benefit of the image processing techniques in identifying the significant characteristics from a detected object. That is, ANN-based methods may be more suitable to detect the desired object, to determine the presence of the desired object, and to segment the desired object, as illustrated in many previous studies [18; 19; 20; 21; 22; 23; 24; 25]. However, this study has some limitations. First, as our initial attempt to inspect cracks on the concrete structure, the proposed method was trained and verified with limited data. Although this proof-of-concept study demonstrated the potential of the proposed method, these limited data points cannot fully represent the variety of concrete cracks. Therefore, the proposed method should be further verified with a larger data set in order to be utilized in the practical field. Related work is currently underway. Second, the accuracy of the proposed method was investigated in one-sized images, although varied-sized images can obviously be applied to the method. If the size of the input image is increased, it can require more computational cost. This might lead to reducing the efficiency of crack inspection. For rigorous validation, it would be necessary to investigate the robustness of the image size. This will be performed in our future work. Third, the proposed crack measurement method is able to calculate the physical length and width of the crack when the physical length of a pixel is given. Although the proposed measurement method was successfully verified using ideal data including the physical length of a pixel, images that are taken in the practical field do not contain physical length data. One possible solution is to shoot a crack together with a certain reference mark that has a predetermined shape and length. The reference mark can help estimate the physical length of a pixel. Lastly, although this study aims to propose a novel concrete crack inspection method, the effects of hyper-parameter changes in the proposed neural network should be further analyzed with a larger training data set prior to practical use to suggest appropriate parameter values more rigorously. The present study proposed a novel crack inspection technique for concrete structures using artificial neural networks and image processing. As described in the preceding sections, the proposed method can detect crack objectively and reliably against test data. Moreover, the proposed method enables accurate measurement of crack characteristics. If the proposed method was verified and validated with a large data set in further research, the proposed method would apply to various types of ground concrete structures such as tunnels, water and sewer pipelines, underground roads, and utility tunnels. The method will also contribute to evaluating the stability and integrity of concrete structures in the long term by efficiently inspecting and managing records of cracks in various types of structures with relatively low-cost portable equipment. Conceptualization, S.-W.L. ; methodology and validation, J.J.K. and A.-R.K.; writing--original draft preparation, J.J.K. and A.-R.K.; writing--review and editing, J.J.K. and S.W.L.