Computer Vision-Based Adhesion Quality Inspection Model for Exterior Insulation and Finishing System

The adhesion quality is one of the critical factors affecting EIFS quality. The traditional visual inspection method is time-consuming and labor-intensive work because limited inspectors have to check all insulation units when they are installed. Thus, this study proposes a CV-based automatic adhesion quality-inspection model for EIFS that can quickly and quantitatively inspect the shape and amount of adhesive. In the case study, the proposed models accurately determined the adhesion quality with measurement accuracies of 90.58%, 95.28%, and 93.58% in ribbon width, dab diameter, and adhesive area, respectively, and a root mean-square error of 6.9 mm in dab distance. The results of this study can achieve automation of inspection for EIFS.

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Publication Details

Journal
Applied Sciences
Published
2024-12-27
DOI
https://doi.org/10.3390/app15010125
Citations
3
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.78

Funders

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article

Computer Vision-Based Adhesion Quality Inspection Model for Exterior Insulation and Finishing System

Mingyun Kang, Taehoon Kim, Sebeen Yoon
3 citations
Applied Sciences
Infrastructure Maintenance and Monitoring
0.78
article

Computer Vision-Based Adhesion Quality Inspection Model for Exterior Insulation and Finishing System

Mingyun Kang, Taehoon Kim, Sebeen Yoon
article en
3 citations

Abstract

The adhesion quality is one of the critical factors affecting EIFS quality. The traditional visual inspection method is time-consuming and labor-intensive work because limited inspectors have to check all insulation units when they are installed. Thus, this study proposes a CV-based automatic adhesion quality-inspection model for EIFS that can quickly and quantitatively inspect the shape and amount of adhesive. In the case study, the proposed models accurately determined the adhesion quality with measurement accuracies of 90.58%, 95.28%, and 93.58% in ribbon width, dab diameter, and adhesive area, respectively, and a root mean-square error of 6.9 mm in dab distance. The results of this study can achieve automation of inspection for EIFS.

Applied SciencesVol. 15(1)
Seoul National University of Science and Technology (KR)
National Research Foundation, Ministry of Land, Infrastructure and Transport, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Decent work and economic growth
Openalex Percentile: Top 29%
Infrastructure Maintenance and Monitoring
0.78
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