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Recently, dense pseudo-label, which directly selects pseudo labels from theoriginal output of the teacher model without any complicated post-processingsteps, has received considerable attention in semi-supervised object detection(SSOD). However, for the multi-oriented and dense objects that are common inaerial scenes, existing dense pseudo-label selection methods are inefficientand impede the performance in semi-supervised oriented object detection.Therefore, we propose Adaptive Dense Pseudo Label Selection (ADPLS) forsemi-supervised oriented object detection. In ADPLS, we design a simple buteffective adaptive mechanism to guide the selection of dense pseudo labels.Specifically, we propose the mean Feature-Richness Score (mFRS) to estimate thedensity of potential objects and use this score to adjust the number of densepseudo labels. On the DOTA-v1.5 benchmark, the proposed method outperformsprevious methods especially when labeled data are scarce. For example, itachieves 49.78 mAP given only 5% of annotated data, which surpasses previousstate-of-the-art method given 10% of annotated data by 1.15 mAP. Our codes willbe available soon.

Adaptive Dense Pseudo Label Selection for Semi-supervised Oriented Object Detection