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Few-shot object detection in optical remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Computer Science, Wuhan University, Wuhan 430072, China

  • Email：2017302580169@whu.edu.cn
  • Introduction：周莲，研究方向为目标检测。E-mail：2017302580169@whu.edu.cn
ZHOU Lian1,  
  • Affiliation：

    School of Electronic Information, Wuhan University, Wuhan 430072, China

HE Chu2,  
  • Affiliation：

    School of Computer Science, Wuhan University, Wuhan 430072, China

WANG Dingwen1,  
  • Affiliation：

    School of Computer Science, Wuhan University, Wuhan 430072, China

GUO Ziqi1

Resümee

Objects in remote sensing images are detected by determining the positions and correct categories of objects. Given that this approach has broad application prospects and plays a vital role in many fields, the purpose of this paper is to study its issues, such as insufficient feature extraction, difficultly in locating objects, and confusing classification, when applied to small samples.The specific contributions of this paper are as follows: (1) A collaborative attention module is proposed, which includes designed background attenuating attention and spatial perception attention. The network focuses on key information related to object positioning on the basis of rich background and object feature information, and an RPN network generates improved regional suggestion boxes, reduces the probability of missing targets, and improves the positioning performance of the model for small-sample categories. (2) A contrastive learning branch is designed. Based on the design of the contrast loss function, feature learning is gradually transferred to classifier learning through a joint training strategy, and classification accuracy is improved. (3) A few-shot object detection model based on the fine-tuning transfer learning paradigm is designed, which is divided into basic training and fine-tuning stages. In the basic training stage, the model is trained to learn class-independent parameters with sufficient base class samples. In the fine-tuning stage, a small sample data set is used to enable the target detection model to adapt to specific objects and improve its detection performance. On the basis of the TFA, this article verifies the effectiveness of the proposed algorithm on the remote sensing data sets NWPU, VHR-10, and DIOR. To demonstrate the superiority of our method to other methods for few-shot object detection, we compared our method with TFA, FR, FSODM, DeFRCN, and MFDC. Results show that the proposed algorithm considerably improved the mean average accuracy on NWPU, VHR-10, and DIOR data sets, demonstrating the superiority of the proposed algorithm over the above algorithms. In summary, our method can achieve exceptional detection results from small remote sensing images, exhibiting effectiveness and superiority over other detection methods. We hope that our method can promote further research into few-shot object detection and contribute to the development of this field.

Schlüsselwort

object detection;few-shot learning;remote sensing image;attention mechanism;contrastive learning

References

  1. 1.
    Chen T, Kornblith S, Norouzi M and Hinton G. 2020. A simple framework for contrastive learning of visual representations//Proceedings of the 37th International Conference on Machine Learning. [s.l.]: ACM: 149 [DOI: 10.5555/3524938.3525087]
  2. 2.
    Chen W Y, Liu Y C, Kira Z, Wang Y C F and Huang J B. 2019. A closer look at few-shot classification//7th International Conference on Learning Representations. New Orleans: ICLR
  3. 3.
    Fan Q, Zhuo W, Tang C K and Tai Y W. 2020. Few-shot object detection with attention-RPN and multi-relation detector//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE: 4012-4021
  4. 4.
    Kang B, Liu Z, Wang X. Few-shot object detection via feature reweighting[A]. Proceedings of the IEEE/CVF International Conference on Computer Vision[C], 2019: 8420-8429.
  5. 5.
    Karlinsky L, Shtok J, Harary S, Schwartz E, Aides A, Feris R, Giryes R and Bronstein A M. 2019. RepMet: representative-based metric learning for classification and few-shot object detection//IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE: 5192-5201
  6. 6.
    Liu X B, Liu P, Cai Z H, Qiao Y L, Wang L and Wang M. 2021. Research progress of optical remote sensing image object detection based on deep learning. Acta Automatica Sinica, 47(9): 2078-2089
  7. 7.
    Li X, Deng J, Fang Y. Few-shot object detection on remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-14.
  8. 8.
    Niemeyer J, Rottensteiner F and Soergel U. 2014. Contextual classification of lidar data and building object detection in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 152-165
  9. 9.
    Qiao L, Zhao Y, Li Z. Defrcn: Decoupled faster r-cnn for few-shot object detection[A]. Proceedings of the IEEE/CVF International Conference on Computer Vision[C], 2021: 8681-8690.
  10. 10.
    Shi J R, Wang D, Shang F H and Zhang H Y. 2021. Research advances on stochastic gradient descent algorithms. Acta Automatica Sinica, 47(9): 2103-2119
  11. 11.
    Sun B, Li B H, Cai S C, Yuan Y and Zhang C. 2021. FSCE: few-shot object detection via contrastive proposal encoding//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE: 7348-7358
  12. 12.
    Wang X, Huang T E, Darrell T. Frustratingly simple few-shot object detection[J].arXiv preprint arXiv:2003.06957, 2020.
  13. 13.
    Wu S, Pei W, Mei D. Multi-faceted Distillation of Base-Novel Commonality for FewShot Object Detection[A]. Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel,October 23-27, 2022, Proceedings, Part IX[C], 2022: 578-594.
  14. 14.
    Yang Z, Wang Y L, Chen X Y, Liu J Z and Qiao Y. 2020. Context-transformer: tackling object confusion for few-shot detection//Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York: AAAI: 12653-12660
  15. 15.
    Yao H G, Wang C, Yu J, Bai X J and Li W. 2020. Recognition of small-target ships in complex satellite images. Journal of Remote Sensing (in Chinese), 24(2): 116-125
  16. 16.
    Yao Q L, Hu X and Lei H. 2019. Aircraft detection in remote sensing imagery with multi-scale feature fusion convolutional neural networks. Acta Geodaetica et Cartographica Sinica, 48(10): 1266-1274
  17. 17.
    Yao Y Q, Cheng G, Xie X X and Han J W. 2021. Optical remote sensing image object detection based on multi-resolution feature fusion. National Remote Sensing Bulletin, 25(5): 1124-1137
  18. 18.
    Yu Y, Ai H, He X J, Yu S H, Zhong X and Zhu R F. 2020. Attention-based feature pyramid networks for ship detection of optical remote sensing image. Journal of Remote Sensing (in Chinese), 24(2): 107-115
  19. 19.
    Zhang T F, Zhang Y, Sun X, Sun H, Yan M L, Yang X and Fu K. 2019. Comparison network for one-shot conditional object detection.arXiv.1904.02317[DOI:10.48550].
  20. 20.
    Zhang W L and Wang Y X. 2021. Hallucination improves few-shot object detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE: 13003-13012
  21. 21.
    Zhang W L, Wang Y X and Forsyth D A. 2020. Cooperating RPN’s improve few-shot object detection. arXiv: 2011.10142
  22. 22.
    Zhang Z W, Hao J G, Huang J and Pan C Y. 2022. Review of few-shot object detection. Computer Engineering and Applications, 58(5): 1-11
  23. 23.
    Zhou P C, Cheng G, Yao X W and Han J W. 2021. Machine learning paradigms in high-resolution remote sensing image interpretation. National Remote Sensing Bulletin, 25(1): 182-197

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