4 Conclusion

In this paper, we propose a framework to explore insulator defect detection under circumstances that combine incomplete annotation and sample imbalance. The framework introduces a PU-RPN that integrates improved PU learning with RPN and incorporates focal loss into the ROI Head. On one hand, the improved PU loss is used to address the problem of incomplete annotation by appropriately calibrating the losses of different samples. In addition, the proposed Pi-Index strategy is responsible for estimating a more accurate class prior by combining classification confidence scores from RPN and predicted boxes from ROIHead. On the other hand, focal loss is incorporated into the ROI Head to alleviate performance degradation caused by sample imbalance. To verify the effectiveness of our proposed framework, we conducted two groups of experiments. The experimental results demonstrate that our method outperforms not only the baseline method or Faster RCNN, but also other mainstream methods. Specifically, our method achieved the highest AP metrics (88.11% for 1.0 APC, 79.57% for 0.7 APC, 73.49% for 0.5 APC, and 63.82% for 0.3 APC) with different proportions of annotations when compared to mainstream methods.
Funding Statement: This research is partially supported by the R&D Program of Beijing Municipal Education Commission (KM202210009003), the National Natural Science Foundation of China (62001009), and the Scientific Research Foundation of North China University of Technology.
Author Contributions: Author Contributions: Conceptualization, F. Q. P.; Methodology, F. Q. P., C.Y. L. and J. S. Z.; Validation, J. S. Z., C. Y. L.; Writing—original draft preparation, F. Q. P., and C. Y. L; Writing—review and editing, J. S. Z., and C. Y. L. All authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.
Availability of Data and Materials: The Insulator Defect Image Dataset refers to: https://ieee-dataport.org/competitions/insulator-defect-detection
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.