Semi-Supervised Object Detection with Multi-Scale Regularization and Bounding Box Re-Prediction

Abstract

Semi-supervised object detection has become a hot topic in recent years, but there are still some challenges regarding false detection, duplicate detection, and inaccurate localization. This paper presents a semi-supervised object detection method with multi-scale regularization and bounding box re-prediction. Specifically, to improve the generalization of the two-stage object detector and to make consistent predictions related to the image and its down-sampled counterpart, a novel multi-scale regularization loss is proposed for the region proposal network and the region-of-interest head. Then, in addition to using the classification probabilities of the pseudo-labels to exploit the unlabeled data, this paper proposes a novel bounding box re-prediction strategy to re-predict the bounding boxes of the pseudo-labels in the unlabeled images and select the pseudo-labels with reliable bounding boxes (location coordinates) to improve the model’s localization accuracy based on its unsupervised localization loss. Experiments on the public MS COCO and Pascal VOC show that our proposed method achieves a competitive detection performance compared to other state-of-the-art methods. Furthermore, our method offers a multi-scale regularization strategy and a reliably located pseudo-label screening strategy, both of which facilitate the development of semi-supervised object detection techniques and boost the object detection performance in autonomous driving, industrial inspection, and agriculture automation.