Photovoltaic panel string detection based on prior knowledge and feature learning

Bolin Li; Bolin Cheng; Liang Ye

doi:10.1117/12.3005228

7 March 2024 Photovoltaic panel string detection based on prior knowledge and feature learning

Bolin Li, Bolin Cheng, Liang Ye

Proceedings Volume 13085, MIPPR 2023: Automatic Target Recognition and Navigation; 130850K (2024) https://doi.org/10.1117/12.3005228
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

Image-based photovoltaic panel inspection has become one of the important tasks of photovoltaic power generation. Due to the repeated patterns of photovoltaic string numbers and different specifications, it is easy to cause false detection by traditional template matching or model training methods. In order to solve this problem, this paper proposes a photovoltaic panel string detection method based on prior knowledge and feature learning. First, the infrared inspection image is segmented for the first time by using the difference in radiation characteristics between the photovoltaic string and the environmental background. Subsequently, according to the local features of the border around the PV string template image, feature matching is performed in the segmented foreground area, and then determine the exact position of the string through the closure and integrity of the matching area. Experiments show that this method has high detection accuracy and has good adaptability to complex and diverse string specifications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bolin Li, Bolin Cheng, and Liang Ye "Photovoltaic panel string detection based on prior knowledge and feature learning", Proc. SPIE 13085, MIPPR 2023: Automatic Target Recognition and Navigation, 130850K (7 March 2024); https://doi.org/10.1117/12.3005228

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KEYWORDS

Solar cells

Prior knowledge

Image segmentation

Photovoltaics

Inspection

Education and training

Environmental sensing

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