Federated contrastive self-supervised pre-training for photovoltaic panels defect detection

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.