# Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning
## 1 Introduction  
Aging social infrastructure needs to find damaged areas and continuously monitor them to maintain their unique functions. The observation of crack propagation on the surface of social infrastructure is primarily used as an index to estimate the degree of damage to the structure. It is common to begin the maintenance of infrastructure structures by inspecting surface cracks with human eyes. Depending on the severity of the cracks (crack width over 0.3 mm), it is determined whether to reinforce or inspect them in detail. However, it is challenging to constantly check the state of surface cracks by human visual inspection when structures are located in places that are not easy to access, such as marine bridges, or when the area to be managed is vast, such as road surfaces. In addition, owing to the lack of professional human resources for visual inspection, a structural diagnosis may be performed after some time.  
The causes of structural cracks are presumed to be drying shrinkage of concrete, insufficient cover thickness, insufficient amount of reinforcing bars at the cross section, influence of various loads acting within the design load, and lack of compaction during construction. Worldwide, the strengthening and monitoring of the structures are quite important as this would be a cost-effective solution compared to demolishing and re-building structures. Recently, rapidly developing information and communication technologies based on deep learning artificial intelligence (AI) inference models have been used in various fields, and grafting is actively being attempted in the construction sector [1; 2; 3; 4; 5; 6; 7]. In particular, in the field of visual recognition, deep learning AI technology is expected to make a significant difference to the existing structure observation method that relies on the human eye. Inconstruction, deep learning AI technology in the field of vision is primarily used to detect problematic parts in images. An object detection model [8; 9; 10; 11; 12] that detects cracks or damages in a structure through an AI model and creates a bounding box in the area where the abnormality occurs, or a classification model that incorporates the sliding window technique [13; 14; 15; 16], is used. Segmentation models [17; 18; 19; 20] that classify states in pixel units to provide detailed shape information, such as crack width and length, are also utilized.  
Cracks in structures occur mainly on the surface of ascons or concrete, and even the same material is recognized as different visual information according to environmental changes, such as aging of materials, differences in components, and illumination and humidity. A deep neural network for heterogeneous data acquired from various local sources is a factor that complicates convergence. In particular, the segmentation for the crack area using only a single deep learning inference model is very challenging. It is time to approach a new technique for improving the model performance. This study introduces a stacking ensemble technique using heterogeneous models, departing from the existing method using a single model.  
In this study, we compared the performances of five representative segmentation models using a heterogeneous dataset obtained from more than five studies. In addition, to improve the performance of the deep neural network for locally acquired heterogeneous data, the stacking ensemble technique was introduced, and the model inference results were compared. Based on the comparison results, we quantitatively show performance improvement when using a stacking ensemble network compared to that of a single semantic segmentation model.