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Contrastive label assignment in vehicle detection

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Abstract

Vehicle detection is a critical task that involves identifying and localizing vehicles in a traffic scenario. However, the traditional approach of one-to-one set matching for label assignment, where each ground-truth bounding box is assigned to one specific query, can lead to sparse positive samples. To address this issue, we drew inspiration from contrastive learning and employed contrasting samples generated by feature augmentation, rather than supplementing the complex one-to-many matching in label assignment. Our proposed approach was evaluated on the publicly available GM traffic dataset and Hangzhou traffic dataset, and the results demonstrate that our approach outperforms other state-of-the-art methods, with average precision (AP) improvements of 1.0% and 1.1%, respectively. Overall, our approach effectively handles the sparsity of positive samples in vehicle detection and achieves better performance than existing methods.

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Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to thank AJE (www.aje.com) for its language editing assistance during the preparation of this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (61976188, 61972351 and 62111530300), the Special Project for Basic Business Expenses of Zhejiang Provincial Colleges and Universities (JRK22003), and the Opening Foundation of State Key Laboratory of Virtual Reality Technology and System of Beihang University (VRLAB2023B02).

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Authors and Affiliations

  1. School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China

    Erjun Sun & Di Zhou

  2. MetaX Integrated Circuits(ShangHai) Co.,Ltd., Shanghai, 200000, China

    Erjun Sun

  3. Zhejiang Uniview Technologies Co., Ltd., Hangzhou, 310051, China

    Di Zhou

  4. School of Mathematical and Computational Sciences, Massey University, Auckland, 0632, New Zealand

    Zhaocheng Xu

  5. School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, 310018, China

    Jie Sun & Xun Wang

Authors
  1. Erjun Sun
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  2. Di Zhou
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  3. Zhaocheng Xu
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  4. Jie Sun
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  5. Xun Wang
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Contributions

Erjun Sun: Formal analysis, Writing - original draft preparation. Di Zhou: Conceptualization, Methodology, Writing - review & editing. Zhaocheng Xu: Software, Data curation, Writing - review & editing. Jie Sun: Writing - review & editing. Xun Wang: Writing - review & editing.

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Correspondence to Di Zhou.

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Sun, E., Zhou, D., Xu, Z. et al. Contrastive label assignment in vehicle detection. Appl Intell 53, 29713–29722 (2023). https://doi.org/10.1007/s10489-023-05023-3

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