Citation

Sharmin, Shaila and Sarker, Shatabdi and Ullah Miah, Md Saef and Hossen, Md. Jakir (2025) A Novel DEEP-SN Hybrid Approach for Segmenting and Classifying Solar Panel. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya.

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Abstract

The detection of solar panels is vital for optimizing the efficiency of solar energy systems. This study presents a novel approach for solar panel detection using a hybrid deep learning model, DEEP-SN. The model combines the segmentation power of DeepLabV3 with the classification capabilities of the Swin Transformer. DeepLabV3 leverages a ResNet50 backbone and atrous spatial pyramid pooling (ASPP) to capture multi-scale information for precise image segmentation. With its ability to process long-range dependencies and spatial hierarchies, the Swin Transformer enhances the model’s classification accuracy. We employ a classweighted loss function to han- dle a class imbalance in the dataset, and the OneCycleLR scheduler dynamically adjusts the learning rate during training. The model achieves a validation accuracy of 90%, a test accuracy of 90%, and an F1 score of 0.90, reflecting its strong performance in detecting solar panels. This approach offers a promising solution for automating solar panel detection, contributing to more effective monitoring and management of solar energy systems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Solar Panels
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK9900-9971 Electricity for amateurs. Amateur constructors' manuals
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	17 Mar 2026 02:40
Last Modified:	17 Mar 2026 02:40
URII:	http://shdl.mmu.edu.my/id/eprint/15464

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