## 5 Crack Detection Automation Algorithm Design and System Development for PC Members  
### Deep Learning-Based Crack Detection Automation Process and Algorithm Design  
For the automation of PC member crack detection, image training and recognition were performed using CNN and object detection, one of the deep learning technologies commonly applied in the field of image object recognition [25]. CNN consists of a convolution layer, a pooling layer, and a fully connected layer. To train the CNN model and carry out CNN-based image training, a defect image (i.e., crack defection) dataset was created by collecting images from web sources and by taking actual photos of target images. With the field application in mind, taking photos of target defect images was carried out under conditions similar to the actual inspection environment, where the distance between the target image and the camera (Galaxy S20 Ultra) lens was maintained at about 0.1 m, and the angle was kept constant at 90 degrees. After collecting image data, they were classified 1:1 for crack and non-crack images, respectively, so as to build an image dataset for deep learning. Then, using Labelme, data labeling was performed by segmenting the cracks within the crack images collected for training. Figure 6 below visually shows this process of building an image dataset. As shown in Figure 7, the CNN-based image training and defect detection process proceeded as follows [26; 27; 28]. As shown in Figure 8, the quality inspection starts with selecting the target PC member to be inspected, and the video images of the surface of the selected member are recorded using a mobile device-based camera. The recorded video data is sent to a web server where cracks are automatically recognized through CNN-based image training and recognition. After crack recognition, the operator arbitrarily sets the values of the length and width of the crack to be detected, the crack is classified according to the user setting, and finally, quantified values for the crack length and width are generated. In addition, the detected crack information is stored and managed through a web server database. Figure 5: Classification of AI applicable major quality inspection items for PC members. Figure 6: Image Dataset for CNN-based Object Detection Model. Figure 7: CNN-based image training and defect detection process. The Single Shot Multi-box Detector 300 (SSD300) algorithm, one of the deep learning-based object detection models, was used to design an automated algorithm for detecting surface cracks in PC members delivered on-site [29]. As shown in Figure 9, the working principle of the SSD algorithm is as follows. First, the training image is set to 300 \(\times\) 300 resolution and input to the SSD model; second, it passes through the Visual Geometry Group (VGG) model, and feature maps with different scales are extracted from the image; third, the image goes through convolution by passing through several feature maps with different grid cell scales; finally, defect detection is performed by merging the entire feature map and training the SSD network through the loss function. As for defect detection, the SSD algorithm is faster than Faster R-CNN, another object detection model, and is more accurate than YOLO [30; 31]. Table 2 compares the performance of different deep learning-based object detection models. Figure 8: Deep learning-based smart quality inspection process. Figure 9: SSD Algorithm Structure and Principle of Operation. The automated crack detection algorithm consists of a total of four stages: data training stage, image processing stage, crack feature information extraction stage, and output stage. Through defect detection, members with no cracks are processed normally, whereas members found with cracks go through the process where the crack location is marked with a bounding box, and the crack size is quantified by measuring the width and length.