سابِق|تال

A group object detection framework for remote sensing image object perception

  • role： First author第一作者
  • Affiliation：

    School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

  • Email：zhw@bupt.edu.cn
  • Introduction：张鸿伟，研究方向为计算机视觉。E-mail： zhw@bupt.edu.cn
ZHANG Hongwei1,  
  • Affiliation：

    School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

JIN Lei1,  
  • Affiliation：

    School of Computer Technology and Applications, Qinghai University, Qinghai 810016, China

ZOU Xuechao2,  
  • Affiliation：

    Graduate School, University of Aerospace Engineering, Beijing 101416, China

FANG Yuqiang3,  
  • Affiliation：

    Beijing Institute of Remote Sensing Information, Beijing 100192, China

YIN Lu4,  
  • Affiliation：

    North Institute of Electronic Equipment, Beijing 100071, China

ZHAO Jian5,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Computing, Tsinghua University, Beijing 100084, China

  • Email：jlxing@tsinghua.edu.cn
  • Introduction：兴军亮，研究方向为视觉感知与博弈决策。E-mail： jlxing@tsinghua.edu.cn
XING Junliang6*

ملخص

Optical remote sensing is a widely used technology in aerospace reconnaissance and geological exploration. Visible light images captured by this technology provide a wealth of information and have important applications in intelligence gathering, object monitoring, and situational forecasting. Considerable progress in remote sensing image object perception has been achieved, particularly in ship and airplane detection. However, technical challenges, including with large object-scale variations and numerous small objects, in remote sensing image object perception remain. Existing work has mainly focused on improving boundary box representations, and single-object detection models fail to fully exploit spatial correlation information from surrounding or similar objects. To address the inherent inefficiency of existing remote sensing image object detection algorithms that detect different objects independently, this paper proposes a novel detection framework called group object detection. By detecting the state information of a group object, our framework alleviates problems, such as insufficient perception information and poor reliability of single-object perception, generating reliable multi-object detection results. This paper introduces a concept of group objects and proposes an automated annotation scheme for group objects. By analyzing existing labels on a public dataset, the proposed scheme obtains annotated information with group object labels without manual annotation. Based on the automated annotation of group targets, a group target detection algorithm is presented, which enhances single-object detection results by utilizing the spatial constraints of group objects. Experimental results on the DOTA dataset, a widely-used remote sensing object detection benchmark, demonstrate that the proposed group target detection algorithm outperforms state-of-the-art methods.

مفهوم

remote sensing image;image object;group object detection;automatic annotation;DOTA;object sensing;multi object

References

  1. 1.
    Azimi S M, Vig E, Bahmanyar R, Körner M and Reinartz P. 2019. Towards multi-class object detection in unconstrained remote sensing imagery//Proceedings of the 14th Asian Conference on Computer Vision. Perth: Springer: 150-165
  2. 2.
    Chen X L, Zhao H M, Li P X and Yin Z Y. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2): 133-146
  3. 3.
    Ding J, Xue N, Long Y, Xia G S and Lu Q K. 2019. Learning RoI transformer for oriented object detection in aerial images//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE: 2844-2853
  4. 4.
    Ding J, Xue N, Xia G S, Bai X, Yang W, Yang M Y, Belongie S, Luo J B, Datcu M, Pelillo M and Zhang L P. 2022. Object detection in aerial images: a large-scale benchmark and challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11): 7778-7796
  5. 5.
    Girshick R. 2015. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision. Santiago: IEEE: 1440-1448
  6. 6.
    Lenhart D, Hinz S, Leitloff J and Stilla U. 2008. Automatic traffic monitoring based on aerial image sequences. Pattern Recognition and Image Analysis, 18(3): 400-405
  7. 7.
    Li Q, Geng D, Zhang J F and Gong L X. 2022. Application and trend analysis of remote sensing technology for earthquake emergency investigation. National Remote Sensing Bulletin, 26(10): 1920-1934
  8. 8.
    Lin T Y, Goyal P, Girshick R, He K M and Dollár P. 2017. Focal loss for dense object detection//Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE: 2999-3007
  9. 9.
    Liu Y and Wu L Z. 2016. Geological disaster recognition on optical remote sensing images using deep learning. Procedia Computer Science, 91: 566-575
  10. 10.
    Lopez R and Frohn R. 2017. Remote Sensing for Landscape Ecology: Monitoring, Modeling, and Assessment of Ecosystems. 2nd ed. Boca Raton: CRC Press
  11. 11.
    Ming Q, Zhou Z Q, Miao L J, Zhang H W and Li L H. 2021. Dynamic anchor learning for arbitrary-oriented object detection//Proceedings of the 35th AAAI Conference on Artificial Intelligence. [s.l.]: AAAI: 2355-2363
  12. 12.
    Pan X J, Ren Y Q, Sheng K K, Dong W M, Yuan H L, Guo X W, Ma C Y and Xu C S. 2020. Dynamic refinement network for oriented and densely packed object detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE: 11204-11213
  13. 13.
    Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149
  14. 14.
    Tian Z Z, Zhang H W, Wang K, Liu S Q, Zou Q J, Zhao Z and Chen Y B. 2023. Application of an improved CenterNet in remote sensing images object detection. National Remote Sensing Bulletin, 27(12): 2706-2715
  15. 15.
    Wang J W, Yang W, Li H C, Zhang H J and Xia G S. 2021. Learning center probability map for detecting objects in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 59(5): 4307-4323
  16. 16.
    Xia G S, Bai X, Ding J, Zhu Z, Belongie S, Luo J B, Datcu M, Pelillo M and Zhang L P. 2018. DOTA: a large-scale dataset for object detection in aerial images//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE: 3974-3983
  17. 17.
    Yang X, Yan J C, Feng Z M and He T. 2021. R3Det: refined single-stage detector with feature refinement for rotating object//Proceedings of the AAAI Conference on Artificial Intelligence. [s.l.]: AAAI: 3163-3171
  18. 18.
    Yi R. 2020. The research on digital media recommendation method based on K-means clustering algorithm. Journal of Changchun Institute of Technology (Natural Science Edition), 21(4): 99-102
  19. 19.
    Zhang G J, Lu S J and Zhang W. 2019. CAD-Net: a context-aware detection network for objects in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 10015-10024

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