Meteorite Detection and Tracing with Deep Learning on FPGA Platform

Kuo Kun Tseng, Jiangrui Lin, Haichuan Sun, K. L. Yung, W. H. Ip

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


At present, the strength of space exploration represents the strength of a country. Meteorite exploration is also part of the space field. The traditional meteorite detection and tracking technology are slow and not accurate. With the development of deep learning, computer detection technology becomes more and more accurate and efficient, which makes it possible to improve the accuracy and speed of meteorite detection. In this paper, the deep learning algorithm implemented by FPGA is applied to the meteorite detection, and the popular tracking algorithm is applied to the meteorite tracking, so that the structure of the meteorite detection and tracking system can meet the practical requirements.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018
EditorsShih-Pang Tseng, Jerry Chun-Wei Lin, Bixia Sui, Jeng-Shyang Pan
Place of PublicationSingapore
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9789811358401
Publication statusPublished - 29 May 2019
Event12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018 - Changzhou, China
Duration: 14 Dec 201817 Dec 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018


  • Deep learning
  • FPGA
  • Meteorite detect
  • Tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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