However, such methods based on virtual sample construction have limited improvement in the detection precision of the classes with few samples and require high computing power and data quality in the source domain, as well as poor generalization, and can only perform data enhancement for a certain type of component defects, which requires repeated data expansion and model training operations when new few-shot detection tasks are added in subsequent inspection work. Choosing metric learning to measure different components is easy to operate, but the global features of defective components and normal components are more similar, resulting in metric learning that cannot solve the core problem of defect detection. Fine-tuning is suitable for detection tasks in which the base class is similar to the novel class. [PERSON] et al. [21] conducted pre-training on the YOLOv3 [22] model using the publicly available ImageNet dataset. Subsequently, they fine-tuned the model using a small number of vibration damper and wire clamp samples to achieve few-shot detection. On the other hand, [PERSON] et al. [23] pre-trained Faster R-CNN on artificial samples and fine-tuned it using insulator defects. Ultimately, they achieved a detection accuracy of 62.7% mAP for insulator defects with a real sample size of 184. However, the generalization of fine-tuning is poor and will lead to a decrease in detection precision when the type of components to be detected is changed. In contrast, by dividing the training data into several different small tasks to train the model, meta-learning not only improves the generalizability of the model under few sample conditions but also the training strategy of using fine-tuning for novel classes is applicable to defect detection tasks in which normal components are similar to defective components. Our goal is meta-learning-based object detection for improving the efficiency of transmission line defect detection tasks under insufficient sample size conditions by combining meta-learning with defect detection. Specifically, we propose a meta-learning object detection algorithm (Meta PowerNet) for few-shot defect detection of transmission lines. First, a meta-attention region proposal network is designed to use a support set with prior knowledge and low-level features with detailed information of the query set to improve the quality of the anchor boxes generated by the region's proposal network. Second, for the characteristics of transmission line defect detection, a defect feature reconstruction module is designed to reconstruct the support set meta-features according to normal and defective components, which not only reduces the computation of fusion meta-features of the support set and ROI features of the query set but also improves the fine-tuning effect of the detection head in the stage of meta-test. Third, this method can detect seven classes, including an insulator, vibration damper, and pin and their defects. Finally, the Meta-Attention Region Proposal Network can be embedded in other meta-learning object detection frameworks to effectively improve the detection precision. The rest of this study is organized as follows: Section 2 discusses the related works on meta-learning and meta-learning object detection. Section 3 explains the details for each module. In Section 4, the implementation is explained, including the dataset; experimental parameters; evaluation indicators; and the results of ablation, comparison, and expanding the experiment. Finally, Section 5 concludes the paper and puts forward prospects for future work.