For this reason, an open source dataset consisting of normal, clean and well-maintained solar photovoltaic panels and solar photovoltaic panels containing dust was used.
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This study aims to develop a deep learning-based model for dust detection on photovoltaic panels. The CNN model, constructed using widely adopted deep learning libraries such
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We integrate deep learning techniques and propose DVNET, an end-to-end PV dust detection model that estimates light transmittance using images of PV panels. This model accurately
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This paper provides an extensive review of dust detection techniques for photovoltaic panels. The review is conducted from two main perspectives. Firstly, the p.
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truction in Different Dust Levels and AI-based Bird Droppings Detection Abstract This paper presents an innovative method for automatically detec.
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In this paper, we propose a novel convolutional neural network architecture based on the EfficientNet framework, customized for photovoltaic dust detection.
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In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study
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Dust accumulation significantly degrades the energy output of photovoltaic (PV) panels, particularly in arid and semi-arid regions. While existing studies have separately explored image
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Atmospheric dust deposition on photovoltaic panels leads to dust accumulation, impairing heat dissipation and significantly reducing both the power generation e
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The improved algorithm proposed in this article has significantly improved the efficiency of dust detection on the surface of photovoltaic panels compared to the Adam algorithm, and is suitable
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