한국건축시공학회지 (Journal of the Korea Institute of Building Construction)

  • 제23권2호
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  • Pages.165-173
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  • 2023
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  • 1598-2033(pISSN)
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  • 2233-5706(eISSN)
한국건축시공학회 (The Korean Institute of Building Construction)
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컴퓨터 비전 기반 외단열 공사의 접착제 도포품질 감리 자동화 모델

Computer Vision-based Automated Adhesive Quality Inspection Model of Exterior Insulation and Finishing System

  • Yoon, Sebeen (Department of Architecture, Seoul National University of Science and Technology) ;
  • Kang, Mingyun (Architectural Engineering Program, School of Architecture, Seoul National University of Science and Technology) ;
  • Jang, Hyounseung (Architectural Engineering Program, School of Architecture, Seoul National University of Science and Technology) ;
  • Kim, Taehoon (Architectural Engineering Program, School of Architecture, Seoul National University of Science and Technology)
  • 투고 : 2022.11.18
  • 심사 : 2022.12.16
  • 발행 : 2023.04.20
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초록

본 연구에서는 외단열 공사의 단열재 접착제 도포 품질을 자동으로 감리할 수 있는 모델을 제안하였다. 사례 적용 결과, 영역 분할 모델은 mAP 92.3%의 정확도를 나타냈고, 제안 모델의 접착제 면적 비율 산출 정확도는 98.8%, 접착제 덩어리 중심 간 거리 산출 정확도는 96.7%로 나타났다. 본 연구 결과는 외단열 공사의 감리를 위한 현장투입 인력을 최소화하면서 외단열 공사의 가장 빈번한 하자인 단열재 탈락 하자를 예방할 수 있으며 나아가 외단열 시스템의 활성화에 기여할 수 있을 것으로 판단된다. 향후에는 다양한 환경에서 외단열 공법의 시공 영상을 수집하여 영상 분할 모델의 성능을 높이고, 영상 내에 다수의 단열재가 포함된 경우에도 자동 감리할 수 있는 모델을 개발하고자 한다.

This research proposed a model for automatically monitoring the quality of insulation adhesive application in external insulation construction. Upon case implementation, the area segmentation model demonstrated a 92.3% accuracy, while the area and distance calculation accuracies of the proposed model were 98.8% and 96.7%, respectively. These findings suggest that the model can effectively prevent the most common insulation defect, insulation failure, while simultaneously minimizing the need for on-site supervisory personnel during external insulation construction. This, in turn, contributes to the enhancement of the external insulation system. Moving forward, we plan to gather construction images of various external insulation methods to refine the image segmentation model's performance and develop a model capable of automatically monitoring scenarios with a considerable number of insulation materials in the image.

키워드

과제정보

This work is supported by the Korea Agency for Infrastructure Technology Advancemen(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport(Grant 1615012983). This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT)(No. 1711172933).

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