한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제25권12호
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  • Pages.1777-1783
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  • 2021
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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시멘틱세그멘테이션을 활용한 태양광 패널 고장 감지 시스템 구현

Implementation of Photovoltaic Panel failure detection system using semantic segmentation

  • Shin, Kwang-Seong (Department of Digital Contents Engineering, Wonkwang University) ;
  • Shin, Seong-Yoon (Department of Computer Information Engineering, Kunsan National University)
  • 투고 : 2021.10.06
  • 심사 : 2021.10.20
  • 발행 : 2021.12.31
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초록

대단위 신재생 에너지 발전단지의 효율적인 유지관리를 위해 드론의 활용이 점차 증가하고 있다. 오래전부터 태양광 패널을 드론으로 촬영하여 패널의 유실 및 오염 등을 관리하고 있다. 본 논문에서는 열화상카메라를 장착한 드론을 이용하여 획득된 태양광패널 이미지에서 아크, 단선, 크랙 등의 고장 유무를 판별하기 위해 시멘틱세그멘테이션 기법을 이용한 분류모델을 제안한다. 또한 적은 데이터셋으로도 강인한 분류 성능을 보이는 U-Net의 튜닝을 통해 효율적인 분류모델을 구현하였다.

The use of drones is gradually increasing for the efficient maintenance of large-scale renewable energy power generation complexes. For a long time, photovoltaic panels have been photographed with drones to manage panel loss and contamination. Various approaches using artificial intelligence are being tried for efficient maintenance of large-scale photovoltaic complexes. Recently, semantic segmentation-based application techniques have been developed to solve the image classification problem. In this paper, we propose a classification model using semantic segmentation to determine the presence or absence of failures such as arcs, disconnections, and cracks in solar panel images obtained using a drone equipped with a thermal imaging camera. In addition, an efficient classification model was implemented by tuning several factors such as data size and type and loss function customization in U-Net, which shows robust classification performance even with a small dataset.

키워드

과제정보

Funding : This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (No. NRF-2019R1G1A1087290) Special thanks to Tae-Woo Kim, Eun-Ji Shin and Ye-Ji Choi from the Department of Digital Content Engineering, Wonkwang University, for their contributions to the experimentation and data organization.

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