한국전산구조공학회논문집 (Journal of the Computational Structural Engineering Institute of Korea)

  • 제35권5호
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  • Pages.259-266
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  • 2022
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  • 1229-3059(pISSN)
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  • 2287-2302(eISSN)
한국전산구조공학회 (Computational Structural Engineering Institute of Korea)
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k-근접 이웃 및 비전센서를 활용한 프리팹 강구조물 조립 성능 평가 기술

Assembly Performance Evaluation for Prefabricated Steel Structures Using k-nearest Neighbor and Vision Sensor

  • 방현태 (충남대학교 자율운항시스템공학과) ;
  • 유병준 ((주)스트라드비전) ;
  • 전해민 (한밭대학교 건설환경공학과)
  • Bang, Hyuntae (Department of Autonomous Vehicle System Engineering, Chungnam National University) ;
  • Yu, Byeongjun (StradVision, Inc.) ;
  • Jeon, Haemin (Department of Civil and Environmental Engineering, Hanbat National University)
  • 투고 : 2022.06.21
  • 심사 : 2022.08.24
  • 발행 : 2022.10.31
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초록

본 논문에서는 프리팹 구조물의 품질관리를 위한 딥러닝 및 비전센서 기반의 조립 성능 평가 모델을 개발하였다. 조립부 검출을 위해 인코더-디코더 형식의 네트워크와 수용 영역 블록 합성곱 모듈을 적용한 딥러닝 모델을 사용하였다. 검출된 조립부 영역 내의 볼트홀을 검출하고, 볼트홀의 위치 값을 산정하여 k-근접 이웃 기반 모델을 사용하여 조립 품질을 평가하였다. 제안된 기법의 성능을 검증하기 위해 조립부 모형을 3D 프린팅을 이용하여 제작하여 조립부 검출 및 조립 성능 예측 모델의 성능을 검증하였다. 성능 검증 결과 높은 정밀도로 조립부를 검출하였으며, 검출된 조립부내의 볼트홀의 위치를 바탕으로 프리팹 구조물의 조립 성능을 5% 이하의 판별 오차로 평가할 수 있음을 확인하였다.

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.

키워드

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