月球表层采样样品智能确认方法

表层采样是月球采样探测的重要方式，样品智能确认有助于提升工作效率与复杂问题处理能力。结合月球表层采样铲挖工作过程，分析了铲挖过程中臂载相机图像的特点，模仿有人参与识别过程，提出了层次解耦的月球样品智能识别流程，利用深度学习方法构建了一类深度卷积识别网络，完整地描述了图像、特征、标记在网络中的正反传递关系，并在月球表层采样地面试验中进行了验证，结果表明该方法对不同光照、不同背景、不同过程、不同形态的样品，具有较好的泛化识别能力，误识别率优于8.1%，平均单幅识别时间约0.7 s。

An Intelligent Approach for Identification of Lunar Surface Sampling Soil

Surface sampling is an important approach in a lunar exploration mission. Intelligent sample identification will greatly contribute to enhance the efficiency and abilities of dealing with complex problems. The characteristics of the robotic arm camera (RAC) imaging are analyzed with the lunar surface excavation workflow. Imitating human identification process, the decoupled intelligent identification workflow is proposed, which has a hierarchical structure. And a class of deep convolutional neural network (CNN) is constructed for soil sample identification with deep learning method. The forward and backward relations of the network are deduced between image, feature and label space. The intelligent identification method is verified in ground surface excavation experiments. The simulation results indicate that the proposed method has preferable generalization in different illumination, scenes, processes, and sample shapes. The statistical error identification rate is less than 8.1%, and the average single image identification time is about 0.7 second.