Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled objects to the unlabeled ones, pseudo-labels for unlabeled objects must be generated. Although pseudo-labels have proven to improve the performance of semi-supervised object detection significantly, the applications of image-based methods to video frames result in numerous miss or false detections using such generated pseudo-labels. In this paper, we propose a new approach, PseudoProp, to generate robust pseudo-labels by leveraging motion continuity in video frames. Specifically, PseudoProp uses a novel bidirectional pseudo-label propagation approach to compensate for misdetection. A feature-based fusion technique is also used to suppress inference noise. Extensive experiments on the large-scale Cityscapes dataset demonstrate that our method outperforms the state-of-the-art semi-supervised object detection methods by 7.4% on mAP75.

本文提出了一种新方法PseudoProp，通过在视频帧中利用运动连续性来生成稳健的伪标签，采用双向伪标签传播方法抵消误检测，同时使用基于特征的融合技术抑制推断噪声，实验证明本方法在大规模Cityscapes数据集上提高了7.4％的平均准确性。

PseudoProp:自动驾驶系统中半监督目标检测的鲁棒伪标注生成