한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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딥러닝을 이용한 PCB 필름 미박리 양품 판정

Determination of PCB film of Un-peeling Defect Using Deep Learning

  • 이정구 (한국과학기술정보연구원) ;
  • 배영철 (전남대학교 전기.전자통신.컴퓨터공학부)
  • 투고 : 2022.09.30
  • 심사 : 2022.12.17
  • 발행 : 2022.12.31
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초록

최근 인공지능 알고리즘으로 대표되는 머신러닝 및 딥러닝 알고리즘이 다양한 분야에서 예측, 분류, 군집화 등과 같은 분야에 적용하고자 하는 노력이 지속되고 있다. 이에 본 논문에서는 PCB의 보호용 필름의 미박리 상태를 디젝트론2를 이용하여 검출하는 알고리즘을 제시한다. 반사 임계각 42.8°의 조건으로 촬영된 이미지로 61장의 데이터를 기반으로, 42장의 데이터를 학습에 19장의 데이터를 검증에 사용하였다. 딥러닝을 이용한 PCB 미박리 필름 검출 결과 19장의 검증 데이터 중 16장 검출, 3장 오검출 결과를 얻었다.

Recently, the effort is continuously applied in machine learning and deep learning algorithm which is represented as artificial intelligence algorithm in the varies field such as prediction, classification and clustering. In this paper, we propose detection algorithm for un-peeling status of PCB protection film by using Dectron2. We use 42 images of data as training and 19 images of data as testing based on 61 images which was taken under the condition of a critical reflection angel of 42.8°. As a result, we get 16 images that was detected and 3 images that was not detected among 19 images of testing data.

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참고문헌

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