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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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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Developing efficient surface contaminants and defect detection algorithms for PV panels can facilitate automated and intelligent maintenance by robotic systems in large-scale PV power
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These results highlight the effectiveness of combining deep feature representations with classical classifiers in multi-class image classification tasks, particularly for applications such as...
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This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust,
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At present, the main methods for detecting surface dust on solar photovoltaic panels include object detection, image segmentation and instance segmentation, super-resolution image
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Recent advancements in machine vision, computer vision, and image processing have driven significant research into automated detection of surface defects in in PV panels.
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This paper, a novel CNN model named SolPowNet is proposed as a lightweight deep learning architecture with high computational efficiency and low hardware requirements for the
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In view of the inefficiency and high cost of manual detection, this paper proposes the use of convolutional neural networks (CNNs) for the automatic recognition and classification of solar panel
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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
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