Recent advancements in deep learning have introduced new solutions for object detection and image segmentation.
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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
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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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Lightweight CNN models that can operate with a lower hardware capacity and provide instantaneous decisions in real-time applications are needed in literature. This study aims to develop
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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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In order to use this balanced dataset in the classification phase with deep learning models, the dataset was divided into 80% training and 20% testing.
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This paper aims to construct an effective convolutional neural network model with hyperparameter tuning using the Equilibrium optimizer (EO) for accurately recognizing dust on solar panels and shows that
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In this work, we developed an artificial vision algorithm based on CIELAB color space to identify dust over panels in an automatic way. The proposed algorithm uses a series of images of
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To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly available dataset to identify the one that gives maximum classification
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arning algorithms to identify dust levels and bird or insect droppings accurately. The experimental setup in Gazipur, Bangladesh, found that excessive dust can block up to 55% of visible
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