## 5 Conclusions and Future Work  
We propose a meta-learning-based method for few-shot defect detection in transmission lines, which aims to address the problem of insufficient samples in the task. First, a region proposal network that combines support set features to generate anchor boxes is proposed to improve the quality of anchor boxes. Second, a meta-learner based on the defect feature reconstruction module is designed to improve the detection accuracy of defect components by considering the data characteristics of normal and defect components in transmission lines. Experimental results show the effectiveness of the Meta PowerNet compared with other mainstream few-shot object detection methods. Moreover, the proposed MA-RPN can be extended to other meta-learning-based object detection methods to enhance detection performance.  
As for future work, we plan to divide the detection process into two steps. First, traditional object detection methods will be employed to locate the key components to be detected. Subsequently, the region of interest will be enlarged based on the detected results, and the enlarged image will be input into a meta-learning image classification network for defect recognition. This approach can not only avoid the problem of local feature loss but can also improve the detection accuracy of the base class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method**\(\backslash\)**Shot** & **1** & **5** & **10** \\ \hline Meta R-CNN [27] & 54.1 & 63.8 & 65.0 \\ Meta R-CNN [27] + MA-RPN & 54.7 & 65.2 & 65.5 \\ Attention RP [36] & 37.8 & 56.5 & 58.6 \\ Attention RPN [36] + MA-RPN & 38.1 & 58.4 & 59.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of expansion effects of MA-RPN.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Base Classes**} & \multicolumn{5}{c}{**Novel Classes**} & \multirow{2}{*}{**mAP**} \\ \cline{2-2} \cline{6-11}  & **Ins 1** & & & & & & & & & \\ \hline FSRW [25] & 80.7 & 79.5 & 46.3 & 68.8 & 76.0 & 72.3 & 76.8 & 42.7 & 66.9 & 67.7 \\ MPSR [35] & 82.9 & 81.4 & 50.1 & 71.4 & 78.6 & 72.5 & 79.5 & 45.3 & 68.9 & 70.0 \\ Meta R-CNN [27] & 82.6 & 80.9 & 49.5 & 71.0 & 77.8 & 70.2 & 79.2 & 45.1 & 68.0 & 69.3 \\ Attention RPN [36] & 83.1 & 81.7 & 50.6 & 71.8 & 79.8 & 73.8 & 80.9 & 46.5 & 70.2 & 70.9 \\ FSCE [14] & 84.5 & 82.6 & 51.1 & 72.7 & 81.2 & 75.3 & 81.7 & 48.0 & 71.8 & 72.2 \\ Meta PowerNet & 84.7 & 83.1 & 51.7 & 73.1 & 81.9 & 76.2 & 82.4 & 49.8 & 72.5 & 72.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of this method and mainstream few-shot object detection.  
**Author Contributions:** Conceptualization, X.Z. and Y.S.; methodology, Y.S. and H.W.; software, Y.S. and H.W.; validation, Y.S., H.W. and C.J.; writing--original draft preparation, Y.S.; writing--review and editing, C.J. and X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key R&D Program of Shanxi Province, grant number 2022 ZDYF100.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** The PASCAL VOC 2007 dataset with the few-shot annotations is available at [[https://github.com/open-mmlab/mmfewshot/blob/main/tools/data/detection/voc/README.md](https://github.com/open-mmlab/mmfewshot/blob/main/tools/data/detection/voc/README.md)]([https://github.com/open-mmlab/mmfewshot/blob/main/tools/data/detection/voc/README.md](https://github.com/open-mmlab/mmfewshot/blob/main/tools/data/detection/voc/README.md)) (accessed on 5 May 2023). For privacy reasons, the self-built dataset cannot be made fully public. Readers can contact the corresponding author for details.  
**Acknowledgments:** Additionally, the authors would like to thank all the reviewers who participated in the review.  
**Conflicts of Interest:** The authors declare no conflict of interest.