Identify solar panel defects by using differences between solar panels

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic solar panel inspection systems are essential to maintain power generation efficiency and reduce the cost. Thermal images generated by thermographic cameras can be used for solar panel fault diagnosis because defective panels show abnormal temperature. However, it is difficult to identify an anomaly from a single panel image when similar temperature features appear in normal panels and abnormal panels. In this paper, we propose a different feature based method to identify defective solar panels in thermal images. To determine abnormal panel from input panel images, we apply a voting strategy by using the prediction results of subtraction network. In our experiments, we construct two datasets to evaluate our method: the clean panels dataset which is constructed by manually extracted panel images and the noise containing dataset which is consisting of panel images extracted by the automatic panel extraction method. Our method achieves more than 90% classification accuracy on both clean panels dataset and noise containing dataset.

Original languageEnglish
Title of host publicationFifteenth International Conference on Quality Control by Artificial Vision
EditorsKenji Terada, Akio Nakamura, Takashi Komuro, Tsuyoshi Shimizu
PublisherSPIE
ISBN (Electronic)9781510644267
DOIs
Publication statusPublished - 2021
Event15th International Conference on Quality Control by Artificial Vision - Tokushima, Virtual, Japan
Duration: May 12 2021May 14 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11794
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Conference on Quality Control by Artificial Vision
Country/TerritoryJapan
CityTokushima, Virtual
Period5/12/215/14/21

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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