# Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection Source: [Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:39:44.122Z Authors: Tong Zhao, Qiang Fang, Shuohao Shi, Xin Xu Published: 2023-11-21T13:49:28.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()