Identifying solar panel defects with a CNN

R. Sireyjol M.D.; P. Granberg M.D.; A. Shimada; T. Minematsu; R. Taniguchi

doi:10.1117/12.2522098

16 July 2019 Identifying solar panel defects with a CNN

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

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111720J (2019) https://doi.org/10.1117/12.2522098
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

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.

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R. Sireyjol M.D., P. Granberg M.D., A. Shimada, T. Minematsu, and R. Taniguchi "Identifying solar panel defects with a CNN", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720J (16 July 2019); https://doi.org/10.1117/12.2522098

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KEYWORDS

Solar cells

Image processing

Solar processes

Human-machine interfaces

RGB color model

Thermography

Image classification

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