Deep Learning Prediction Frame Matching Algorithm of Small Celestial Navigation Landmarks

Abstract

Deep learning algorithm has a higher recognition rate for navigation landmarks such as small meteor craters than traditional algorithms，but it is difficult to achieve matching under various image changes. To solve this problem，a description method of recognition prediction box based on feature descriptor was proposed，and the matching of recognition results was completed. Firstly，the circular support region of the recognition prediction frame was determined and a 10-dimensional feature descriptor with rotation and translation scale and luminance invariance was constructed and the prediction frame was matched by the relative distance between descriptor vectors. The results show that the proposed algorithm is robust to images under different transformations，and the correct matching rate of the prediction frame is over 90%. It may provide the reference for the asteroid exploration navigation system.

Keywords

deep learning / navigation landmarks / support area / feature descriptor / prediction frame matching

References

[1] 崔平远,贾贺,朱圣英,等. 小天体光学导航特征识别与提取研究进展[J]. 宇航学报,2020,41(7):880-888
CUI P Y,JIA H,ZHU S Y,et al. Research progress on optical navigation feature recognition and extraction technologies for small body exploration[J]. Journal of Astronautics,2020,41(7):880-888
[2] 张荣桥,黄江川,赫荣伟,等. 小行星探测发展综述[J]. 深空探测学报(中英文),2019,6(5):417-423,455
ZHANG R Q,HUANG J C,HE R W,et al. The development overview of asteroid exploration[J]. Journal of Deep Exploration,2019,6(5):417-423,455
[3] 夏元清. 火星探测器进入、下降与着陆过程的导航、制导与控制：“恐怖”七分钟[M]. 北京：科学出版社，2018.
[4] 崔平远,葛丹桐,朱圣英,等. 行星着陆点自主评估与选取研究进展[J]. 中国科学:技术科学,2021,51(11):1315-1325
CUI P Y,GE D T,ZHU S Y,et al. Research progress of autonomous planetary landing site assessment and selection[J]. Scientia Sinica Technologica,2021,51(11):1315-1325
[5] EMAMI E,AHMAD T,BEBIS G,et al. Crater detection using unsupervised algorithms and convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing,2019,57(8):5373-5383
[6] TIAN Y,YU M,YAO M B,et al. Crater edge-based flexible autonomous navigation for planetary landing[J]. The Journal of Navigation,2019,72(3):649-668
[7] 于正湜,朱圣英,马冬梅,等. 行星表面非规则陨石坑检测与识别方法[J]. 宇航学报,2013,34(3):320-326
YU Z S,ZHU S Y,MA D M,et al. Detection and recognition method for irregular craters on planetary surface[J]. Journal of Astronautics,2013,34(3):320-326
[8] CHRISTOFF N，MANOLOVA A，JORDA L，et al. Morphological crater classification via convolutional neural network with application on MOLA data[C]//Advances in Neural Networks and Applications. St. Konstantin and Elena Resort， Bulgaria：IEEE，2018.
[9] EMAMI E，AHMAD T，BEBIS G，et al. Lunar crater detection via region-based convolutional neural networks[C]//The 49th Lunar and Planetary Science Conference. Woodlands，Texas：LPI，2018.
[10] WANG H,JIANG J,ZHANG G. CraterIDNet:an end-to-end fully convolutional neural network for crater detection and identification in remotely sensed planetary images[J]. Remote Sensing,2018,10(7):1067
[11] RONNEBERGER O，FISCHER P，BROX T. U-net：Convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference. Munich，Germany：Springer，2015.
[12] LEE C. Automated crater detection on mars using deep learning[J]. Planetary and Space Science,2019,170:16-28
[13] CHEN S,LI Y,ZHANG T,et al. Lunar features detection for energy discovery via deep learning[J]. Applied Energy,2021,296:117085
[14] 郑磊,胡维多,刘畅. 基于深度学习的大型陨石坑识别方法研究[J]. 北京航空航天大学学报,2020,46(5):994-1004
ZHENG L,HU W D,LIU C. Large crater identification method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics,2020,46(5):994-1004
[15] FURLÁN F，RUBIO E，SOSSA H，et al. Rock detection in a mars-like environment using a CNN[C]//Mexican Conference on Pattern Recognition. Mexico：Springer，2019.
[16] LI H C,QIU L W,LI Z,et al. Automatic rocks segmentation based on deep learning for planetary rover images[J]. Journal of Aerospace Information Systems,2021,18(11):755-761
[17] SONG J,RONDAO D,AOUF N. Deep learning-based spacecraft relative navigation methods:A survey[J]. Acta Astronautica,2022,191:22-40
[18] KIM C，FUXIN L，ALOTAIBI M，et al. Discriminative appearance modeling with multi-track pooling for real-time multi-object tracking[C]//IEEE International Conference on Computer Vision. Montreal：IEEE，2021.
[19] DAI P，WENG R，CHOI W，et al. Learning a proposal classifier for multiple object tracking[C]//IEEE International Conference on Computer Vision. Montreal：IEEE，2021.
[20] SUN P，CAO J，JIANG Y，et al. Dancetrack：multi-object tracking in uniform appearance and diverse motion[C]//IEEE International Conference on Computer Vision. Changchun，China：IEEE，2022.
[21] CIOPPA A，GIANCOLA S，DELIEGE A，et al. Soccernet-tracking：multiple object tracking dataset and benchmark in soccer videos[C]//IEEE International Conference on Computer Vision. Changchun，China：IEEE，2022.
[22] DANELLJAN M，HÄGER G，KHAN F S，et al. Convolutional features for correlation filter based visual tracking[C]//IEEE International Conference on Computer Vision Workshops. Santiago，Chile：IEEE，2015.
[23] HELD D，THRUN S，SAVARESE S. Learning to track at 100 fps with deep regression networks[C]//14th European Conference on Computer Vision. Amsterdam，The Netherlands：Springer，2016.
[24] DANELLJAN M，HÄGER G，KHAN F，et al. Accurate scale estimation for robust visual tracking[C]//British Machine Vision Conference. Nottingham，UK：BMVA Press，2014.
[25] ZHOU X，YIN T，KOLTUN V，et al. Global tracking transformers[C]//IEEE International Conference on Computer Vision. Changchun，China：IEEE，2022.
[26] POSSEGGER H，MAUTHNER T，BISCHOF H. In defense of color-based model-free tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Santiago，Chile：IEEE，2015.
[27] 邵巍,郗洪良,王光泽,等. 暗弱环境下小天体陨石坑智能检测算法[J]. 宇航学报,2021,42(11):1439-1445
SHAO W,XI H L,WANG G Z,et al. An intelligent detection algorithm for small body craters in faint environment[J]. Journal of Astronautics,2021,42(11):1439-1445