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%.

Key words: Lunar landing exploration, Crater detection, Deep learning, Convolutional neural networks, Lightweight processing