重利用不可靠伪标签的单阶段半监督目标检测OA

The key to semi-supervised object detection methods is to assign pseudo labels to the targets of unlabeled data.To guarantee the quality of pseudo-labels,the semi-supervised object detection methods usually use a confidence threshold to filter low-quality pseudo-labels,which will cause most pseudo-labels to be removed due to their low confidence.Contrastive learning is used to reuse most of low-confidence unreliable pseudo labels for boosting the performance of semi-supervised object detection method.Specifically,the pseudo-labels are divided into reliable and unreliable ones according to the prediction confidence.Be-sides the reliable pseudo-labels,the unreliable pseudo-labels are exploited as negative samples for model training of contrast learning.To balance the number of unreliable pseudo-labels between different classes,a memory module is designed to store the unreliable pseudo-labels of different batches in the training process.The experimental results show that the mAP of the improved semi-supervised method on COCO data set is 13.6%,23.0%,and 27.5%with the labeling ratio of 1%,5%,and 10%,which is better than the existing semi-supervised learning methods.On the COCO-additional data set,the mAP of the improved semi-supervised method reaches 44.7%,which is 4.5 percentage points higher than supervised learning.