Citation

Lim, Jia Min and Lim, Kian Ming and Lee, Chin Poo and Chen, Zhiyuan and Low, Cheng Yaw and Lim, Jit Yan (2026) DINO-Proto: DINO-Augmented Prototypical Network for Few-Shot Photovoltaic Fault Classification. In: 3rd International Conference on Algorithms, Software Engineering and Network Security, ASENS 2026, 24 March 2026 - 26 March 2026, Guangzhou.

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Abstract

Photovoltaic (PV) anomaly detection plays a critical role in ensuring the reliability and efficiency of solar energy systems. However, existing diagnostic approaches typically rely on large amounts of annotated thermal imagery, which is expensive and labor-intensive in real-world inspection scenarios. Although few-shot learning provides a promising paradigm for addressing data scarcity by enabling effective recognition from limited labeled samples, its application to PV thermal inspection remains underexplored, particularly due to the complex and subtle textural variations present in infrared imagery. To address these challenges, we propose DINO-Proto, a novel fewshot learning framework built upon a Prototypical Network architecture with a DINO (Self-Distillation with No Labels)- inspired data augmentation strategy. Within the proposed DINOProto framework, a lightweight convolutional feature encoder is trained using diverse augmented data that reflect environmental conditions commonly encountered in real-world photovoltaic (PV) inspections, thereby improving class separability under limited supervision. We evaluate DINO-Proto on the Infrared Solar Modules dataset with 11 PV fault categories under various few-shot settings. Extensive experimental results demonstrate that the proposed approach consistently outperforms state-of-theart few-shot methods, achieving superior classification accuracy as a result of more compact intra-class distributions under datalimited conditions

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Prototypical Networks, DINO Data Augmentation
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Sep 2026 06:40
Last Modified:	03 Sep 2026 06:40
URII:	http://shdl.mmu.edu.my/id/eprint/16622

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