  Recently, dense pseudo-label, which directly selects pseudo labels from the
original output of the teacher model without any complicated post-processing
steps, has received considerable attention in semi-supervised object detection
(SSOD). However, for the multi-oriented and dense objects that are common in
aerial scenes, existing dense pseudo-label selection methods are inefficient
because they ignore the significant density difference. Therefore, we propose
Density-Guided Dense Pseudo Label Selection (DDPLS) for semi-supervised
oriented object detection. In DDPLS, we design a simple but effective adaptive
mechanism to guide the selection of dense pseudo labels. Specifically, we
propose the Pseudo Density Score (PDS) to estimate the density of potential
objects and use this score to select reliable dense pseudo labels. On the
DOTA-v1.5 benchmark, the proposed method outperforms previous methods
especially when labeled data are scarce. For example, it achieves 49.78 mAP
given only 5\% of annotated data, which surpasses previous state-of-the-art
method given 10\% of annotated data by 1.15 mAP. Our codes is available at
https://github.com/Haru-zt/DDPLS.
