月表陨检测轻化深度学习方法

Translated title of the contribution: Lightweight Deep Learning Method for Lunar Surface Crater Detection

Ai Gao*, Yongjun Zhou, Junwei Wang, Zezhao Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A lightweight deep learning crater detection method is proposed to address the problems of large number of model parameters and slow detection of the current deep learning crater detection methods. Firstly, the channel pruning method is used to delete the redundant convolution kernel in convolution neural network to obtain a compact and efficient crater detection model. Then, the lightweight depthwise separable convolution operation is used to replace the standard convolution operation in the basic crater detection model, which further reduces the complexity of the model. The simulation results show that the proposed lightweight crater detection model can ensure high pixel prediction accuracy, and can adapt to the influence of interference factors such as brightness and image noise. Moreover, compared with the model before lightweight processing, the amount of parameters is reduced by 99.2% and the detection speed is improved by 94%.

Translated title of the contributionLightweight Deep Learning Method for Lunar Surface Crater Detection
Original languageChinese (Traditional)
Pages (from-to)830-838
Number of pages9
JournalYuhang Xuebao/Journal of Astronautics
Volume43
Issue number6
DOIs
Publication statusPublished - 1 Jun 2022

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Gao, A., Zhou, Y., Wang, J., & Wu, Z. (2022). 月表陨检测轻化深度学习方法. Yuhang Xuebao/Journal of Astronautics, 43(6), 830-838. https://doi.org/10.3873/j.issm.1000-1328.2022.06.014