Semi-Supervised Object Detection (SSOD) has been successful in improving the performance of both R-CNN series and anchor-free detectors. However, one-stage anchor-based detectors lack the structure to generate high-quality or flexible pseudo labels, leading to serious inconsistency problems in SSOD. In this paper, we propose the Efficient Teacher framework for scalable and effective one-stage anchor-based SSOD training, consisting of Dense Detector, Pseudo Label Assigner, and Epoch Adaptor. Dense Detector is a baseline model that extends RetinaNet with dense sampling techniques inspired by YOLOv5. The Efficient Teacher framework introduces a novel pseudo label assignment mechanism, named Pseudo Label Assigner, which makes more refined use of pseudo labels from Dense Detector. Epoch Adaptor is a method that enables a stable and efficient end-to-end semi-supervised training schedule for Dense Detector. The Pseudo Label Assigner prevents the occurrence of bias caused by a large number of low-quality pseudo labels that may interfere with the Dense Detector during the student-teacher mutual learning mechanism, and the Epoch Adaptor utilizes domain and distribution adaptation to allow Dense Detector to learn globally distributed consistent features, making the training independent of the proportion of labeled data. Our experiments show that the Efficient Teacher framework achieves state-of-the-art results on VOC, COCO-standard, and COCO-additional using fewer FLOPs than previous methods. To the best of our knowledge, this is the first attempt to apply Semi-Supervised Object Detection to YOLOv5.

翻译：半监督目标检测（SSOD）在提升R-CNN系列和无锚框检测器性能方面已取得显著成效。然而，单阶段锚框检测器缺乏生成高质量或灵活伪标签的结构，导致SSOD中存在严重的非一致性难题。本文提出高效教师框架，用于可扩展且有效的单阶段锚框SSOD训练，该框架由密集检测器、伪标签分配器与周期适配器三大组件构成。密集检测器是基于RetinaNet的基线模型，通过引入受YOLOv5启发的密集采样技术进行扩展。高效教师框架创新性地提出伪标签分配机制——伪标签分配器，可更精细化地利用密集检测器生成的伪标签。周期适配器是一种支持密集检测器进行稳定高效的端到端半监督训练的调度方法。在师生互学机制中，伪标签分配器能有效抑制大量低质量伪标签对密集检测器造成的偏差干扰；而周期适配器通过域适应与分布适应技术，使密集检测器学习全局分布式一致特征，从而将训练过程与标注数据比例解耦。实验表明，高效教师框架在VOC、COCO-standard和COCO-additional数据集上以更低的FLOPs实现了最先进性能。据我们所知，这是首次将半监督目标检测应用于YOLOv5的尝试。