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Solar panel surface dust detection method based on DLS-Net deep learning framework

• 1、Department of Electronic and Information EngineeringLiaoning Technical University

Abstract：The accumulation of dust on solar panel surfaces severely affects photovoltaic power generation efficiency, making accurate detection of dust contamination levels crucial for maintenance and operation. To address issues in traditional Convolutional Neural Networks (CNNs) for dust detection, such as gradient degradation, parameter redundancy, and insufficient fine-grained feature extraction capability, this study proposes a multi-level attention-enhanced model based on dense convolutional networks, termed DLS-Net (DenseNet-based LPA-SSA Network). The model employs DenseNet-121 as the foundational feature extraction framework, achieving feature reuse and effective gradient propagation through dense connectivity mechanisms. It incorporates a Local Pyramid Attention (LPA) module to enable adaptive fusion of multi-scale spatial features, enhancing the perception capability for dust coverage areas of varying sizes. Furthermore, it integrates a Sequence Shuffle Attention (SSA) module to facilitate cross-group channel information interaction and deeply exploit complementary relationships among features. Experiments conducted on a dataset containing 1,187 solar panel images demonstrate that DLS-Net achieves an accuracy of 98.90%, representing a 2.68 percentage point improvement over the baseline DenseNet-121 model, with an F1 score of 97.62%. Ablation experiments validate the effectiveness of each module, and Grad-CAM visualization results indicate that the model can accurately localize key regions of dust features. This research provides an efficient and reliable detection solution for intelligent operation and maintenance of solar panels.

Keywords： Neural Network Deep learning Dust detection Solar panels