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23 August 2024 Federated contrastive self-supervised pre-training for photovoltaic panels defect detection
Fengyao Xu, Zhaodian Wu
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Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132502Z (2024) https://doi.org/10.1117/12.3038604
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia
Abstract
To address the challenges of data security, labor-intensive data annotation, and limited data availability in photovoltaic panel defect detection, a federated contrastive self-supervised pre-training method FCSP is proposed in this paper for photovoltaic panels defect detection. We utilize federated learning to collaboratively pre-train feature extraction backbone in a data-security manner to achieve better model initialization weights. Subsequently, each client loads the federated pretrained weights and conducts supervised fine-tuning with local labeled data for defect detection model training. Experimental results demonstrate that, compared to independent training locally, FCSP achieves an improvement on defect detection, thereby validating the effectiveness of the proposed method.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Fengyao Xu and Zhaodian Wu "Federated contrastive self-supervised pre-training for photovoltaic panels defect detection", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132502Z (23 August 2024); https://doi.org/10.1117/12.3038604
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KEYWORDS
Defect detection
Photovoltaics
Data privacy
Object detection
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