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

Mingyun Kang, Sebeen Yoon, Taehoon Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus 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.

Original languageEnglish
Article number125
JournalApplied Sciences (Switzerland)
Volume15
Issue number1
DOIs
StatePublished - Jan 2025

Keywords

  • computer vision
  • exterior insulation and finishing system
  • instance segmentation
  • polygon with hole
  • quality management

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