SolPowNet: Dust Detection on Photovoltaic Panels Using
Mehta and Singh developed automatic dust detection systems that optimize solar energy production and proposed a CNN-SVM-based method for this process. In their study, CNN was
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Mehta and Singh developed automatic dust detection systems that optimize solar energy production and proposed a CNN-SVM-based method for this process. In their study, CNN was
Dust accumulation can reduce PV system efficiency, resulting in unstable energy output. To effectively detect and monitor dust impact on PV systems, this paper proposes a dust recognition
At present, the main methods for detecting surface dust on solar photovoltaic panels include object detection, image segmentation and instance segmentation, super-resolution image
In this research, we propose an integrated approach that combines image processing techniques and deep learning-based classification for the identification and classification of dust on
Experimental results demonstrate that our model achieves 87.31% accuracy in detecting dust on solar panel surfaces. Under the same experimental conditions and dataset, this model
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
Various environmental factors such as dust, snow, pollen, and bird droppings can affect the full penetration of sunlight onto the solar panels, reducing their electricity production...
Solar dust detection is crucial for maintaining solar panel efficiency, especially in dusty environments. This study uses a Kaggle dataset, to train and evaluate baseline Dense and Convolutional Neural
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
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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