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Source-free domain adaptive object detection based on pseudo-supervised mean teacher

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Abstract

Domain adaptive object detection refers to training a cross-domain object detector through a large number of labeled source domain datasets and unlabeled target domain datasets and learning the domain invariant features between two domains to reduce or eliminate the domain discrepancy. However, factors such as data privacy protection, limited storage space, and high labor costs often make many source domain-labeled samples unavailable in real-time situations. In this work, we propose a pseudo-supervised mean teacher model for source-free domain adaptive object detection that alternates between generating pseudo-labels and fine-tuning the model and utilizes a pixel-level distillation loss method and the weight regularization module for model adaptation. We use the mean teacher model to assist training to achieve object detection task in the source-free domain. Experiments are carried out on multiple datasets such as Cityscapes, Foggy Cityscapes, and SIM10K. Extensive experiments on multiple domain adaptation scenarios show that our method achieves better performance than the baseline (Faster R-CNN) and multiple state-of-the-art domain adaptation methods which require access to source domain data, demonstrating the effectiveness and robustness of the proposed method.

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Availability of data and materials

Data openly available in a public repository. Cityscapes: https://www.cityscapes-dataset.com/downloads/ Pascal VOC: http://host.robots.ox.ac.uk/pascal/VOC/ SIM10K: https://fcav.engin.umich.edu/projects/driving-in-the-matrix

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Acknowledgements

This work was supported by Joint Fund of Natural Science Foundation of Anhui Province in 2020 (2008085UD08), Anhui Provincial Key R &D Program (202004a05020004), Open fund of Intelligent Interconnected Systems Laboratory of Anhui Province (PA2021AKSK0107), Intelligent Networking and New Energy Vehicle Special Project of Intelligent Manufacturing Institute of HFUT (IMIWL2019003, IMIDC2019002).

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Emerald Road 420, Hefei City, 230601, Anhui Province, China

    Xing Wei, Ting Bai, Yan Zhai & Yang Lu

  2. Intelligent Manufacturing Institute of Hefei University of Technology, Hefei University of Technology, Emerald Road 420, Hefei City, 230051, Anhui Province, China

    Xing Wei, Hui Luo & Chong Zhao

  3. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei City, 230009, Anhui Province, China

    Xing Wei & Yang Lu

  4. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei City, 230031, Anhui Province, China

    Lei Chen

  5. Engineering Quality Education Center of Undergraduate School, Hefei University of Technology, Hefei City, 230601, Anhui Province, China

    Chong Zhao

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  1. Xing Wei
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  2. Ting Bai
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  3. Yan Zhai
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  4. Lei Chen
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Correspondence to Ting Bai.

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Wei, X., Bai, T., Zhai, Y. et al. Source-free domain adaptive object detection based on pseudo-supervised mean teacher. J Supercomput 79, 6228–6251 (2023). https://doi.org/10.1007/s11227-022-04915-4

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