Identifying solar panel defects with a CNN

R. Sireyjol, P. Granberg, A. Shimada, T. Minematsu, R. Taniguchi

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

Abstract

With the development of green energy and its means of production, more and more companies chose to build solar panel farms. However, those technologies remain relatively expensive to maintain, and prone to damages (due to natural hazards, or internal defects). Since any kind of damage on a panel cell drastically reduce a panel's efficiency, solar panels must be kept under tight supervision. With more solar panel that must be checked for damage relatively often, a cheap, accurate and fast way to find those damages must be settled. Some processes have been developed to identify panels in a true color image [1], and various ways to identify defective panels exist through image processing [2], [3] or other ways [4]. On another hand, handmade features suggest the input data obeys to some specific conditions (color, illumination), and small changes can impact accuracy. CNN [5], however, can be trained to face such changes with the appropriate dataset, and therefore be more resilient. They represent a reliable solution for identification and classification of complex features [2], [6], and can be improved more easily than handmade feature detection. In this paper is detailed the pipeline of such process, combining the straightforward approach of handmade feature detection for preprocessing to reduce the input's complexity, with the resilience of neural networks for the final identification. Detailed explanations for the different steps of the process are given: Dataset acquisition, preprocessing, and finally classification. The various leads that were followed to improve the quality of the results are also given, before comparing results with a previously used handmade detection process, and finally proposing a web user interface to exploit this process, and enrich its dataset.

Original languageEnglish
Title of host publicationFourteenth International Conference on Quality Control by Artificial Vision
EditorsChristophe Cudel, Stephane Bazeille, Nicolas Verrier
PublisherSPIE
ISBN (Electronic)9781510630536
DOIs
Publication statusPublished - 2019
Event14th International Conference on Quality Control by Artificial Vision, QCAV 2019 - Mulhouse, France
Duration: May 15 2019May 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11172
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference14th International Conference on Quality Control by Artificial Vision, QCAV 2019
CountryFrance
CityMulhouse
Period5/15/195/17/19

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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