High Power Laser and Particle Beams, Volume. 36, Issue 6, 069001(2024)

Research of aircraft pose estimation based on neural network feature line extraction

Changjun Chen1,2, Dan Tang1、*, Hao Yang1, Anqing You1, and Xudong Pan1
Author Affiliations
  • 1Institute of Applied Electronics, CAEP, Mianyang 621900, China
  • 2Graduate School, China Academy of Engineering Physics, Beijing 100088, China
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    To estimate the aircraft pose in complex situation, this paper proposes a new method of aircraft pose estimation based on neural network line extraction. This method uses 3D model to render images, and forms dataset through adding backgrounds. The dataset is enhanced to make the algorithm robust. The line extraction model uses convolutional neural network to extract deep features, and uses heatmap to obtain aircraft feature lines. The target pose is solved by combining the aircraft feature line, the aircraft 3D model and the perspective-n-line method. The accuracy of the line extraction model is 91% in complex background. The accuracy is 84% after addingall sorts of noises. The aircraft pose is solved by using EPnL algorithm and nonlinear optimization. The average angle error is about 0.57°, and the average translation error is about 0.47% when the target is in a complex background. After addingall sorts of noises to the image, the average angle error is about 2.11°, and the average translation error is about 0.93%. The aircraft pose estimation method proposed in this article can accurately predict the aircraft pose under complex backgrounds and various types of noise, and its application scenarios are more extensive.

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    Changjun Chen, Dan Tang, Hao Yang, Anqing You, Xudong Pan. Research of aircraft pose estimation based on neural network feature line extraction[J]. High Power Laser and Particle Beams, 2024, 36(6): 069001

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    Paper Information

    Category: Advanced Interdisciplinary Science

    Received: Jan. 23, 2024

    Accepted: Mar. 6, 2024

    Published Online: Jun. 3, 2024

    The Author Email: Tang Dan (278803565@qq.com)

    DOI:10.11884/HPLPB202436.240032

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