## 1 Introduction  
Numerous nations around the globe are actively pursuing strategies to reduce greenhouse gas emissions in a bid to achieve carbon neutrality by 2050. A significant contributor to these efforts is the building sector, which accounts for approximately 40% of global energy consumption [1, 2, 3]. One promising solution to address challenges in the building sector is the realization of Zero-Energy Buildings (ZEBs), where the Exterior Insulation and Finishing System (EIFS) plays a pivotal role.  
EIFS, initially developed in Europe under the name External Thermal Insulation Composite System (ETHICS), is a cladding method that provides an insulated and finished exterior wall surface. Over time, these features have been integrated into EIFS, including waterproofing and drainage functions, refining it into a comprehensive exterior insulation system [4]. This system is particularly effective in reducing heat loss, which accounts for more than half of a building's total energy demand [3, 4, 5, 6].  
The adhesion quality is one of the critical factors affecting the overall performance of EIFS. The most common method of attaching insulation to the structure is by using an adhesive similar to mortar [7]. Poor adhesion quality can result in the detachment of insulation from the wall and pose fire safety risks due to the chimney effect caused by unbonded spaces between the insulation and the wall [8]. Due to these safety concerns, many countries and organizations have established regulations for EIFS adhesion quality. These regulations include specifications regarding adhesive quantity, shapes, bonding strength, cracks, and moisture curing, among other factors.  
The traditional inspection method, which involves inspectors checking visually whether the shape and amount of adhesive meet EIFS specifications, makes it difficult to ensure the adhesion quality of EIFS. It is time-consuming and labor-intensive work because limited inspectors have to check all insulation units when they are installed. Also, inspectors can make subjective judgements by visual checks.  
Recently, with the development of deep learning, computer vision (CV)-based approaches can aid supervision in various construction tasks, serving as a substitute for human eyes. Many researchers are working on applying deep-learning-based CV techniques to the construction industry. However, applying CV to the adhesion quality of EIFS has not been sufficiently explored.  
To address this gap, this study proposes a CV-based adhesion quality-inspection model for EIFS. As previously mentioned, while EIFS adhesion quality includes various factors, this study focuses on enabling CV techniques to quickly and quantitatively inspect the shape and amount of adhesive.  
The proposed model consists of three steps: first is the segmentation step to extract the insulation and adhesive from the background. The second is the image transformation step to convert the tilted insulation image into a front-facing image, and the third is the measurement step to measure the shape and amount of adhesive.  
The remainder of the paper is organized as follows. Section 2 reviews the relevant studies and EIFS specifications to demonstrate the contributions of the study. Section 3 presents the detailed model proposed in this study. In Section 4, the case study is conducted to validate the proposed model. Finally, Section 5 presents the summary, contributions, and limitations.