Opto-Electronic Engineering, Volume. 49, Issue 4, 210363(2022)

Small object detection based on multi-scale feature fusion using remote sensing images

Liang Ma1,2,3, Yutao Gou1,2,3, Tao Lei1,2、*, Lei Jin1,2, and Yixuan Song1,2,3
Author Affiliations
  • 1Photoelectric Detection Technology Laboratory, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 2Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    References(23)

    [1] [1] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580–587.

    [2] [2] Girshick R. Fast R-Cnn[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 1440–1448.

    [3] [3] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015, 28: 91–99.

    [4] [4] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//Proceedings ofthe 14th European Conference on Computer Vision, 2016: 21–37.

    [5] [5] Redmon J, Farhadi A. YOLOV3: an incremental improvement[Z]. arXiv: 1804.02767, 2018. https://doi.org/10.48550/arXiv.1804.02767

    [6] [6] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 2999–3007.

    [7] [7] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936–944.

    [8] [8] Fu C Y, Liu W, Ranga A, et al. DSSD: deconvolutional single shot detector[Z]. arXiv: 1701.06659, 2017. https://arxiv.org/abs/1701.06659

    [9] [9] Li Z X, Zhou F Q. FSSD: feature fusion single shot multibox detector[Z]. arXiv: 1712.00960, 2017. https://doi.org/10.48550/arXiv.1712.00960

    [10] [10] Cui L S, Ma R, Lv P, et al. MDSSD: multi-scale deconvolutional single shot detector for small objects[Z]. arXiv: 1805.07009, 2018. https://doi.org/10.48550/arXiv.1805.07009

    [11] [11] Liang Z W, Shao J, Zhang D Y, et al. Small object detection using deep feature pyramid networks[C]//Proceedings of the 19th Pacific Rim Conference on Multimedia, 2018: 554–564.

    [13] [13] Zhang S F, Wen L Y, Bian X, et al. Single-shot refinement neural network for object detection[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4203–4212.

    [20] [20] Gong Y Q, Yu X H, Ding Y, et al. Effective fusion factor in FPN for tiny object detection[C]//Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision, 2021: 1159–1167.

    [21] [21] Xia G S, Bai X, Ding J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 3974–3983.

    [22] [22] Ding J, Xue N, Xia G S, et al. Object detection in aerial images: a large-scale benchmark and challenges[Z]. arXiv: 2102.12219, 2021. https://doi.org/10.48550/arXiv.2102.12219

    [29] [29] Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009: 248–255.

    [31] [31] Yang X, Yang J R, Yan J C, et al. SCRDet: towards more robust detection for small, cluttered and rotated objects[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, 2019: 8231–8240.

    [32] [32] Azimi S M, Vig E, Bahmanyar R, et al. Towards multi-class object detection in unconstrained remote sensing imagery[C]//Proceedings of the 14th Asian Conference on Computer Vision, 2018: 150–165.

    [33] [33] He Y H, Xu D Z, Wu L F, et al. LFFD: a light and fast face detector for edge devices[Z]. arXiv: 1904.10633, 2019. https://doi.org/10.48550/arXiv.1904.10633

    [34] [34] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[Z]. arXiv: 1409.1556, 2014. https://doi.org/10.48550/arXiv.1409.1556

    [35] [35] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.

    [36] [36] Zhu C C, He Y H, Savvides M. Feature selective anchor-free module for single-shot object detection[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 840–849.

    [37] [37] Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 3–19.

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    Liang Ma, Yutao Gou, Tao Lei, Lei Jin, Yixuan Song. Small object detection based on multi-scale feature fusion using remote sensing images[J]. Opto-Electronic Engineering, 2022, 49(4): 210363

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    Paper Information

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    Received: Nov. 15, 2021

    Accepted: --

    Published Online: May. 24, 2022

    The Author Email: Lei Tao (taoleiyan@ioe.ac.cn)

    DOI:10.12086/oee.2022.210363

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