PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems
| Autor: | Hu, Shu, Liu, Chun-Hao, Dutta, Jayanta, Chang, Ming-Ching, Lyu, Siwei, Ramakrishnan, Naveen |
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| Rok vydání: | 2022 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted by the Workshop on Autonomous Driving (WAD) at CVPR 2022 |
| Databáze: | arXiv |
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