月表陨坑检测轻量化深度学习方法

针对目前基于深度学习的陨坑检测方法存在的模型参数量大和检测速度慢的问题，提出了一种轻量化的深度学习陨坑检测方法。首先，采用通道剪枝方法删减卷积神经网络中冗余的卷积核，得到结构紧凑高效的陨坑检测模型。然后，使用轻量化的深度可分离卷积操作替换基础陨坑检测模型中的标准卷积操作，进一步降低了模型的复杂度。仿真实验结果表明，所提出的轻量化陨坑检测模型能够保证较高的像素预测精度，并且能够适应亮度、图像噪声等干扰因素的影响。同时，与轻量化处理前的模型相比，参数量减少了99.2%，检测速度提升了94%。

Lightweight Deep Learning Method for Lunar Surface Crater Detection

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%.