## 5 Conclusions  
This study proposed an automatic inspection model of EIFS using a single image taken from the construction site. The proposed model consists of the segmentation step to extract the insulation and adhesive from the background, the image transformation step to align the tilted insulation image into a front-facing insulation image by using the perspective transformation, and the measurement step to inspect the adhesive shape, including ribbon width, dab distance, dab diameter, and the amount of adhesive on the insulation.  
The proposed model was validated through a case study. The validation of the proposed model is divided into two parts: the performance of the instance segmentation model and the performance of the proposed model. In the case of the instance segmentation model, the accuracy of the model trained with 240 images was mAP50:95 of 0.86. In the case of the proposed model, the error was 9.42% for ribbon width, 4.72% for dab diameter, 6.42% for adhesive area, and RMSE 6.9 mm for dab distance. As a result, the proposed model was found to be valid.  
This study can contribute to the body of knowledge as follows. First, the proposed model can quickly and quantitatively inspect the shape and amount of the adhesive, which are inspection items in the EIFS specification. It can overcome the traditional inspection method's limitations, which are time-consuming and labor-intensive work. Second, the effectiveness of applying CV to the adhesion quality of EIFS is proven. The CV-based model in this study can measure the adhesive shape, including ribbon width, dab distance, dab diameter, and the amount of adhesive on an insulation with a high accuracy. Finally, this study's methodology can be used for measuring the size or area of other objects in the construction site from a single image. For example, it can be used to measure the finished area of a wall and the spacing between supports.  
Figure 16: EIFS adhesive quality determination based on threshold metrics.  
The limitations of this study have been discussed in the section on results analysis and discussion. First, heavily tilted images can lead to distortion during the image transformation step. Second, variations in ribbon thickness, such as thinner ribbons, can affect the accuracy of inspection results due to oversegmented outputs.  
Additionally, the number of images used for training the instance segmentation model is relatively small and was collected from a limited number of sites. As a result, the proposed model cannot guarantee high performance in other environments.  
For further studies, the performance of the proposed model will be improved by increasing its robustness under varied image conditions and by acquiring a larger dataset from diverse environments. This will enhance the accuracy and generalizability of the instance segmentation model.  
Conceptualization, T.K.; Methodology, M.K.; Software, S.Y.; Formal analysis, M.K.; Writing--original draft, M.K.; Visualization, S.Y.; Supervision, T.K.; Project administration, T.K.; Funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (RS-2022-00143493) from Digital-Based Building Construction and Safety Supervision Technology Research Program funded by Ministry of Land, Infrastructure and Transport of Korean Government and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A4A3026883).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.