the proposed method is based on the Faster RCNN, this section also selected Faster RCNN as the baseline of PN-based two-stage detectors.
For the fair comparison, we chosen two PU-based detectors that adopted Faster RCNN as the base model. These two methods are detailed in Section 3.4. The first one is Pi-GS (Grid Search) [49], which estimates the class prior probability by conducting a grid search on the validation set with a search interval of 0.1 and repeating training 10 times. The second method is Pi-FT (Fixed Threshold) [34], which utilizes a fixed threshold to filter out the positive anchors by comparing their confidence scores with this threshold. In summary, we use M-YOLO v3, D-YOLO v3, M-YOLO v4, D-YOLO v4, D-YOLOv5, Pi-GS, and Pi-FT as the comparison methods.

4.3 Evaluation metrics

In this paper, we introduce the COCO evaluation metrics, a popular metrics for object detection, from the COCO challenge [50]. The COCO metrics is designed based on the principal metric, mAP@T , which
stands for the mAP with IoU threshold equaling T . For example, AP@0.5 and AP@0.75 are the typical mAP metrics, provided by the COCO metrics. AP in COCO metrics represents the average of mAP with IoU threshold varying from 0.5 to 0.95 (interval 0.05 ). Finally, AP-category is the AP being applied to one particular category, such as AP-String, APGood, AP-Broken, and AP-Flashover-Damage (shorted as AP-FlashoverD) in our experiments.

4.4 Detection results

This section presents the detection results of different methods under 1.0, 0.7, 0.5, and 0.3 Annotation PerCent (APC), as shown in Tables 2-5. Meanwhile, Figures 8-9 visualize the detection results of different methods, providing a more intuitive display of the results. In detail, Figure 8 (I) and (II) depict the detection results under 1.0 and 0.7 APCs, respectively. Figures 9 (I) and (II) individually present the detection results under 0.5 and 0.3 APCs.
4.4.1 Detection results with IDID’s annotations
The IDID dataset is used as a fully annotated dataset in reality. But there are some samples with missing annotations due to the dense arrangement of insulators and oversights by annotators. Some examples of missing annotations are shown in Section 4.1.1. Therefore, even though all the IDID’s annotations are used, it still belongs to incomplete annotation setting. We constructed the first experiment under all annotations provided by the IDID dataset. The detection results are summarized in Table 2, and the visualization of prediction results are shown in Figure 8 (I).