The complexity of annotation in deep learning-based object detection has spurred advancements in semi-supervised target detection techniques.Recently,anchor-free object detectors have been applied in semi-supervised object detection,however existing methods face problems of inadequate feature representation and low quality of pseudo labels.For this purpose,we propose an semi-supervised object detection algorithm based on improved Dense Teacher.Firstly,a feature pyramid(CIE-FPN)based on channel information enhancement is designed to optimize the receptive field and feature fusion of feature maps,so as to capture more global feature information.Secondly,a pseudo-label double-branch processing strategy is adopted to solve the inconsistency between classification and detection tasks,and then improve the robustness of pseudo-labels.Experimental results on the COCO dataset,with only 10％of data annotated,demonstrate significant improvements in anchor-free semi-supervised object detection performance compared to the baseline Dense Teacher network.
维普
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