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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (3): 28796-028796.doi: 10.7527/S1000-6893.2023.28796

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A review of image matching methods based on deep learning

Haiqiao LIU1, Meng LIU1, Zichao GONG1, Jing DONG2()   

  1. 1.School of Electrical and Information Engineering,Hunan Institute of Engineering,Xiangtan 411104,China
    2.Research Institute of Aerospace Technology,Central South University,Changsha 410083,China
  • Received:2023-04-03 Revised:2023-04-24 Accepted:2023-07-06 Online:2024-02-15 Published:2023-07-21
  • Contact: Jing DONG E-mail:dongjing@csu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(62173134);Scientific Research Foundation of Hunan Provincial Department of Education(21B0661)

Abstract:

Image matching is a key technology in aircraft visual navigation. The image matching methods based on deep learning have developed rapidly in recent years. The feature extraction network of the methods has obvious advantages over traditional methods, and has broad prospects for application. The image matching method based on deep learning can be divided into the single-link matching network model method and the end-to-end matching network model matching method according to different network structures. In this paper, the feature detection, descriptor learning, similarity measurement and error elimination network model of the network model of the single-link matching method network model are first investigated and analyzed. Then, the single-network structure method and multi-network structure combination method in the end-to-end matching network model are reviewed, and the classic end-to-end matching network model algorithm is introduced and analyzed. Finally, the problems of current image matching methods based on deep learning are pointed out, and the possible development trend and direction in the future are discussed to provide a certain reference for subsequent research on deep-learning based image matching.

Key words: deep learning, image matching, visual navigation, single link, end-to-end, feature detection

CLC Number: