반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

심층신경망을 이용한 PCB 부품의 검지 및 인식

Detection of PCB Components Using Deep Neural Nets

  • 조태훈 (한국기술교육대학교 컴퓨터공학부)
  • Cho, Tai-Hoon (School of Computer Science and Engineering, Korea University of Technology and Education)
  • 투고 : 2020.04.20
  • 심사 : 2020.06.11
  • 발행 : 2020.06.30
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초록

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

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참고문헌

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