# Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning
## 2 Literature Review  
### Representative Semantic Segmentation Architecture  
Recently, AI models in the field of vision have been rapidly developed with the emergence of convolutional neural networks (CNNs). CNN models are used in various fields, and they are also used for continuous management by detecting anomalies in the construction field. In particular, microcracks on the surface of structures, such as concrete or ascon, require geometric shape information, such as the width and length of cracks, to determine the current state of structural damage, continuously observe the progress of damage, and use them as decision-making data for repair and reinforcement. Segmentation models, which can closely distinguish areas occupied by cracks by analyzing images in pixel units, are mainly used because they can easily determine the shape of microcracks. Therefore, in this study, the analysis focused on semantic segmentation models that can easily detect microcracks on the surface of structures, such as concrete or ascons.  
Semantic segmentation models are based on fully convolutional networks (FCNs). All hidden layers are convolutional, and the pre-learned classification model is used as the feature extractor. The results of pixel-wise prediction are provided through an upsampling process based on the features obtained by the feature extract. If a concrete crack image is input, the location where the crack occurs is provided in pixel units. An FCN [17] is the simplest basic form of a semantic segmentation model. This is simply the result of upsampling the result of passing through a feature extractor to a desired size. According to the detailed steps of upsampling, it is divided into FCN-8s, FCN-16s, and FCN-32s, and the information of each pooling layer of the feature extractor is summarized with the upsampled information.  
A semantic segmentation model based on an FCN was developed into two types of architectures. One is an encoder-decoder type architecture that maximizes the ensemble effect by connecting the tensors in the encoder and decoder with a skip connection. Because of the shape of the network, it was named U-Net [21], and by diversifying upsampling, max-pooling, and skip connection, J-Net, M-net, UNet++, UNet3+, and CU-Net [22; 23; 24; 25] were used in the development. This model prevents the loss of contextual information due to rapid upsampling and exhibits high performance in distinguishing micro-boundaries between objects.  
The other makes the feature extractor more sophisticated. After arranging pooling layers with different receptive field sizes to extract more diverse features, upsampling was performed to generate an output of the desired size. Networks from DeepLabV1 to V3 belong to this category [26; 27; 28]. It uses fewer parameters than UNet and can obtain higher segmentation quality than FCN. In addition, a decoder module was added to the DeepLabV3 model to develop DeepLabV3 plus, which includes encoder-decoder features and encodes richer contextual information to distinguish object boundaries [29].  
In addition, the recent deep learning architecture using the attention module shows high performance in natural language processing, and its use is expanding to the field of vision, for example, dual attention [30] and OCR net [31]. DANet effectively extracts long-range contextual information between pixels of the same class that are far apart from each other by inputting the extracted image features into the position and channel attention modules and then producing results.  
In this study, the performance of the FCN-8s model, which is the basic model of semantic segmentation, and DeepLabV3, DeepLabV3+, UNet, and DANet, were compared, and the segmentation performance improvement was reviewed using the stacking ensemble learning technique.