4.4.2 Detection results under 0.3 annotation percentage
In this section, the APC is set to 0.3, which means that 70% of the labels in the IDID dataset are randomly removed. The aim is to observe the detection performance of each method when labels are severely missing and to verify the effectiveness of the algorithm proposed in this paper under such conditions.
In Tables 2-5, each detector’s performance deteriorated as the APC decreased. Contrary to the results of the previous experiment, Table 5 shows that the AP metric of YOLO v5 is lower than that of D-YOLO v4. It indicates that YOLO v5 is not suitable for the situation where there is a severe lack of labels. Nevertheless, the two-stage Faster RCNN surpasses other PN-based methods with an AP of 58.57%, an AP@0.75 of 70.41%, and an AP@0.5 of 81.02%, which is consistent with previous experiments. According
Table 5: Detection results of our method and the baseline method with (APC=0.3) in the training process.