An improved Yolov5 real-time detection method for small objects captured by UAV-Reference-Cited by-同舟云学术

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An improved Yolov5 real-time detection method for small objects captured by UAV

Published:2021-11-02 Issue:1 Volume:26 Page:361-373
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Zhan Wei^ORCID,Sun Chenfan,Wang Maocai,She Jinhui,Zhang Yangyang,Zhang Zhiliang,Sun Yong

Funder

China Postdoctoral Science Foundation

Aeronautical Science Fund

Natural Science Foundation of Hubei Province

the second batch of Chinese University industry research innovation foundation “new generation information technology innovation project”

Jingzhou science and technology development plan project

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-021-06407-8.pdf

Reference40 articles.

1. Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934

2. Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 6154–6162

3. Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 6:19959–19967

4. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

5. Du D, Zhu (2019) Visdrone-det2019: the vision meets drone object detection in image challenge results. In: 2019 IEEE/CVF international conference on computer vision workshop (ICCVW). pp 213–226. https://doi.org/10.1109/ICCVW.2019.00030

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