针对电力巡检中绝缘子缺陷样本稀缺导致缺陷识别准确率较低的问题，提出一种结合fine-tuning训练策略和cosine similarity softmax（CSM）分类器的识别方法，将基于度量元学习的小样本方法应用于绝缘子缺陷分类任务。该方法主要分为2个步骤：首先，使用大型数据集对神经网络进行预训练；接着，采用fine-tuning训练策略和CSM分类器，结合元学习和度量学习对模型进行优化，将预训练和元学习阶段中学到的相关知识迁移到绝缘子缺陷图像分类领域。通过消融实验，验证了加入fine-tuning训练策略后，召回率提高0.66%以上，精确率提高0.70%以上；加入CSM后，召回率提高0.50%以上，精确率提高0.53%以上。与其他主流小样本学习方法相比，召回率和精确率分别提高0.67%以上和0.62%以上。实验结果表明，该方法在准确性、鲁棒性和泛化能力方面表现出较高的性能，证实了它在电力巡检绝缘子缺陷图像分类任务中的有效性。

To address the issue of low defect recognition accuracy due to the scarcity of insulator defect samples in power inspection， we propose a recognition method that combines a fine-tuning training strategy with a cosine similarity softmax （CSM） classifier. This method applies a few-shot learning based on metric learning to the task of insulator defect classification. The approach consists of two main steps： first， pre-training the neural network on a large dataset； and second， using the fine-tuning training strategy and CSM classifier to optimize the model， integrating meta-learning and metric learning to transfer the relevant knowledge acquired during the pre-training and meta-learning phases to the domain of insulator defect image classification. Ablation experiments demonstrate that incorporating the fine-tuning training strategy increases recall by more than 0.66% and precision by more than 0.70%. Adding the CSM further improves recall by more than 0.50% and precision by more than 0.53%. Compared to other mainstream few-shot learning methods， our method shows improvements in recall and precision of over 0.67% and 0.62%， respectively. Experimental results indicate that the proposed method exhibits high performance in terms of accuracy， robustness， and generalization capability， confirming its effectiveness in the task of insulator defect image classification for power inspection.