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TinyDet: accurately detecting small objects within 1 GFLOPs

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Conclusion

In this study, we propose a series of lightweight detectors named TinyDet. TinyDet is with good performance-computation trade-offs (30.3 mAP with only 991 MFLOPs) and applicable to resource-constrained mobile or edge devices. Besides, TinyDet is superior to other lightweight detectors in small object detection.

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References

  1. Luo W, Li Y, Urtasun R, et al. Understanding the effective receptive field in deep convolutional neural networks. In: Proceedings of International Conference on Neural Information Processing Systems, 2016. 4905–4913

  2. Wang J, Bohn T A, Ling C X. Pelee: a real-time object detection system on mobile devices. In: Proceedings of International Conference on Neural Information Processing Systems, 2018

  3. Qin Z, Li Z, Zhang Z, et al. ThunderNet: towards real-time generic object detection on mobile devices. In: Proceedings of IEEE International Conference on Computer Vision, 2019

  4. Ghiasi G, Lin T, Le Q V. NAS-FPN: learning scalable feature pyramid architecture for object detection. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61876212, 61733007), Zhejiang Laboratory (Grant No. 2019NB0AB02), and HUST-Horizon Computer Vision Research Center.

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Authors and Affiliations

  1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China

    Shaoyu Chen, Tianheng Cheng, Jiemin Fang, Wenyu Liu & Xinggang Wang

  2. Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jiemin Fang

  3. Horizon Robotics Inc., Beijing, 100080, China

    Qian Zhang

  4. Google Inc., Mountain View, CA, 94043, USA

    Yuan Li

Authors
  1. Shaoyu Chen
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  2. Tianheng Cheng
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  3. Jiemin Fang
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  4. Qian Zhang
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  5. Yuan Li
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  6. Wenyu Liu
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  7. Xinggang Wang
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Corresponding author

Correspondence to Xinggang Wang.

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Supporting information

Appendixes A and B. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Chen, S., Cheng, T., Fang, J. et al. TinyDet: accurately detecting small objects within 1 GFLOPs. Sci. China Inf. Sci. 66, 119102 (2023). https://doi.org/10.1007/s11432-021-3504-4

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  • DOI: https://doi.org/10.1007/s11432-021-3504-4

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