Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104440
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Title: Meteorite detection and tracing with deep learning on FPGA platform
Authors: Tseng, KK
Lin, J
Sun, H
Yung, KL 
Ip, WH 
Issue Date: 2019
Source: Advances in intelligent systems and computing, 2019, v. 834, p. 493-500
Abstract: 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.
Keywords: Deep learning
FPGA
Meteorite detect
Tracking
Publisher: Springer
Journal: Advances in intelligent systems and computing 
ISSN: 2194-5357
EISSN: 2194-5365
DOI: 10.1007/978-981-13-5841-8_51
Description: Twelfth International Conference on Genetic and Evolutionary Computing, December 14-17, 2018, Changzhou, Jiangsu, China
Rights: © Springer Nature Singapore Pte Ltd. 2019
This version of of the proceeding paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-981-13-5841-8_51.
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