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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 San Diego, CA, USA. https://doi.org/10.1109/cvpr.2005.177
Lowe, D.G.: Object Recognition from local scale-invariant features. IEEE 2(3), 1150–1157 (1999)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)
He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision, pp. 1026–1034. IEEE (2016)
Girshick, R.: Fast R-CNN. Comput. Sci. (2015)
Ouyang W., Zeng, X., Wang, X. et al.: DeepID-Net: deformable deep convolutional neural networks for object detection. IEEE Trans. Pattern Anal. Mach. Intell. (99), 1–1 (2016)
Redmon, J., Divvala, S., Girshick, R., et al.: You Only Look Once: Unified, Real-Time Object Detect. 779–788 (2015)
Liu, W., Anguelov, D., Erhan, D., et al.: SSD: Single Shot MultiBox Detect. 21–37 (2015)
Huang, K., Reng, W., Tieniu, T.: Survey of image object classification and detection algorithms. J. Comput. Sci. 37(6), 1225–1240 (2014)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-Speed tracking with Kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tseng, KK., Lin, J., Sun, H., Yung, K.L., Ip, W.H. (2019). Meteorite Detection and Tracing with Deep Learning on FPGA Platform. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_51
Download citation
DOI: https://doi.org/10.1007/978-981-13-5841-8_51
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5840-1
Online ISBN: 978-981-13-5841-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)