DustMambaNet: A hybrid InceptionV3–state-space network for robust solar panel dust detection

Document Type

Article

Source of Publication

E Prime Nexus of Electrical Electronic and Intelligent Engineering

Publication Date

9-1-2026

Abstract

To develop a robust, scalable vision-based model for automatic detection and quantification of dust accumulation on solar photovoltaic panels, overcoming limitations of existing convolutional and attention-based methods and supporting proactive maintenance. We propose DustMambaNet, a hybrid model that consists of a pretrained InceptionV3 convolutional neural network as a feature extractor and two selective state space sequence modules. The state space modules use gated depthwise convolutions to represent long-range spatial dependencies that are of linear complexity, after rearranging spatial features to form sequences. The network provides a binary classification of dust with a severity index (DSI) and a continuous one. All dusty and clean panel images were resized to a size of 299×299 pixels, augmented, and divided into training and validation subsets, comprising about 12 440 images. Training consisted of two phases: initial training of the Mamba blocks and head with the backbone frozen, followed by fine-tuning of the top convolutional layers. On the test set, DustMambaNet achieved a mean accuracy of 93.67 %, precision of 0.960, recall of 0.897, and F1-score of 0.927, outperforming seven baseline models by more than 3 % accuracy and exhibiting superior Cohen’s kappa and Matthew’s correlation coefficients. The hybrid network that has been proposed is very efficient in capturing the local texture and global dust patterns distribution, and gives very dependable dust detention and severity measurement. Its scalability and efficiency allow its use in a large-scale solar farm, and the Dust Severity Index provides actionable information on how to schedule the cleaning of panels. DustMambaNet illustrates the power of combining a convolutional and state space model in the maintenance of photovoltaic systems.

ISSN

3117-5112

Publisher

Elsevier BV

Volume

17

Disciplines

Electrical and Computer Engineering

Keywords

Hybrid deep learning architecture, InceptionV3–Mamba network, Photovoltaic maintenance automation, Selective state-space modelling, Solar panel dust detection

Scopus ID

105043220004

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Hybrid: This publication is openly available in a subscription-based journal/series

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