## 4 Discussion  
Crack inspection is implemented by two consecutive processes: crack detection and measurement. This technique is very important in civil engineering because it is directly related to the safety, durability,  
Figure 10: Result of segmentation, thinning, tracking and profiling for the crack image; red-colored and blue-colored circles indicates inaccurate results. and the applicability of concrete structures. Based on this fact, various automatic crack inspection methods have been proposed based on image processing (IP), machine learning (ML) algorithms, and artificial neural networks (ANN). In an objective and efficient manner, these crack inspections showed better performance than the conventional human-based method that relies on the subjectivity of inspectors. Particularly, ANN-based methods were outstanding in crack detection [18; 19; 20; 21; 22; 23; 24; 25]. However, these approaches have rarely been considered in crack measurement [18; 19; 20; 21; 22; 23]. This paper proposed a new framework that can inspect cracks objectively and efficiently in concrete ground structures through crack detection and measurement. The framework detects crack using artificial neural networks that classify crack patches in the input image and segment cracks in the classified patches. Subsequently, it analyzes crack characteristics (i.e., length and width) using image processing techniques. Verification using test data demonstrated that this proposed framework can reliably provide crack characteristics through the consecutive processes. Convolutional neural network (CNN)-based crack classification is proposed in this study. Each layer has a number of convolution filters of different sizes. The convolution filter in the neural networks allows the conversion of an input image into an image that highlights the sought-after characteristics. Specifically, a learned convolution filter can play a role as a Sobel filter [31] that detects edges then creates an image emphasizing the edges. The study consisted of a number of convolution filters of \(5\times 5\) size in each layer from 16 to 128 to build efficient crack classification. In the verification, the framework showed high classification accuracy of about 99%. Moreover, this high accuracy is in very good agreement with state-of-art techniques that use the same training data [20; 21; 25]. These facts demonstrated that the filters in the proposed method were appropriate designed and used for properly classifying crack patches in the input image. This study proposed a patchwise-crack detection method that divides an input image into patch units, then determines the presence of cracks in each patch by the classifier, and segments crack from the classified patches thereafter. It should be noted that this approach can overcome the lack of training data by obtaining multiple image patches from a single image, prevent the decline of architecture performance for high-resolution images, and overcome the imbalanced dataset problems by using only image patches with cracks. For example, the learning process for a segmentation neural network requires at least 1000 training data points that are not only crack images but also crack-labeled images. However, it is exceedingly difficult to manually label cracks in the obtained images from cameras (typically, \(2960\times 1440\) QHD resolution). Since the pixels of crack images may account for less than 1% of the pixels of the entire images, the learning performance can decline significantly due to imbalanced datasets. Based on these facts, this study implemented a patchwise-segmentation neural network and showed that the network has a reasonably high intersection over union (IOU) of 0.87 despite the limited number of 1751 learning data points. This result demonstrates the considerable potential of the proposed method. The proposed method also has another advantage in that it can be applied not only to fixed-sized but also varied-sized images obtained in various environments.