I present the Lower Biased Teacher model, an enhancement of the Unbiased Teacher model, specifically tailored for semi-supervised object detection tasks. The primary innovation of this model is the integration of a localization loss into the teacher model, which significantly improves the accuracy of pseudo-label generation. By addressing key issues such as class imbalance and the precision of bounding boxes, the Lower Biased Teacher model demonstrates superior performance in object detection tasks. Extensive experiments on multiple semi-supervised object detection datasets show that the Lower Biased Teacher model not only reduces the pseudo-labeling bias caused by class imbalances but also mitigates errors arising from incorrect bounding boxes. As a result, the model achieves higher mAP scores and more reliable detection outcomes compared to existing methods. This research underscores the importance of accurate pseudo-label generation and provides a robust framework for future advancements in semi-supervised learning for object detection.

本研究针对半监督目标检测任务提出了低偏差教师模型，该模型通过将定位损失整合到教师模型中，显著提高了伪标签生成的准确性。研究表明，该模型有效降低了由于类别不平衡和边界框不准确导致的伪标签偏差，最终实现了更高的mAP分数和更可靠的检测结果。

在半监督目标检测中应用低偏差教师模型