Semi-Supervised Object Detection with Multi-Scale Regularization and Bounding Box Re-Prediction
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Published:2024-01-03
Issue:1
Volume:13
Page:221
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Shao Yeqin1ORCID, Lv Chang1, Zhang Ruowei2, Yin He3, Che Meiqin1ORCID, Yang Guoqing4, Jiang Quan1
Affiliation:
1. School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China 2. College of Electrical Engineering, Nantong University, Nantong 226004, China 3. School of Information Science and Technology, Nantong University, Nantong 226019, China 4. Suzhou Research Institute of Industrial Technology, Zhejiang University, Hangzhou 310058, China
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.
Funder
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference40 articles.
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