Large crater identification method based on deep learning

ZHENG Lei; HU Weiduo; LIU Chang

doi:10.13700/j.bh.1001-5965.2019.0342

Volume 46 Issue 5

May 2020

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Abstract

References

Journal of Beijing University of Aeronautics and Astronautics > 2020 > 46(5): 994-1004.

Zhang Shuo, Pu Juhua, Liu Yuheng, et al. Coverage quality problem in wireless sensor networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(5): 631-635. (in Chinese)

Citation:

ZHENG Lei, HU Weiduo, LIU Changet al. Large crater identification method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 994-1004. doi: 10.13700/j.bh.1001-5965.2019.0342(in Chinese)

Zhang Shuo, Pu Juhua, Liu Yuheng, et al. Coverage quality problem in wireless sensor networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(5): 631-635. (in Chinese)

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Large crater identification method based on deep learning

doi: 10.13700/j.bh.1001-5965.2019.0342

ZHENG Lei¹,
HU Weiduo^{1
,
,},
LIU Chang²

1.
School of Astronautics, Beihang University, Beijing 100083, China
2.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Funds:

National Natural Science Foundation of China 61703017

More Information

Corresponding author: HU Weiduo, E-mail:08109@buaa.edu.cn
Received Date: 28 Jun 2019
Accepted Date: 29 Sep 2019
Publish Date: 20 May 2020

Abstract

Abstract

Craters are the most significant topographic features on the surface of celestial bodies. The traditional method of craters identification is mainly to study the dichotomy of positive and negative samples of small craters, with low efficiency and accuracy. This paper takes large craters under the macroscopic view of the planet as the research object, combines the knowledge of digital image processing and neural network, creates a crater sample library of different data sources to study the influence of data source on network model generalization ability, and proposes a more efficient crater multi-classification identification method. Based on the Non-Maximum Suppression (NMS) algorithm, a higher precision crater detection algorithm is proposed. Through parameter optimization and experimental verification, the multi-scale and multi-classification craters automatic recognition network framework based on deep learning constructed in this paper achieves a high accuracy rate, with the recognition rate up to 0.985 on homologous verification set and 0.863 on heterogeneous verification set, and effectively improves the redundancy of detection box and false detection in target detection.
- deep learning,
- convolutional neural network,
- crater identification,
- Non-Maximum Suppression (NMS) algorithm,
- object detection

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References(32)

References

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