A Study on Lunar Crater Recognition System Using Binary Neural Networks Implemented on Low Computational Resource Hardware

The Japan Aerospace Exploration Agency (JAXA) aims to develop an image navigation system to accurately estimate the self-position of space probes. In recent years, extensive research has been conducted on image recognition using machine learning. Object detection and homography matrix estimation methods based on convolutional neural networks (CNN) exhibit high discrimination and generalization performance. However, implementing these systems on small-space probes is challenging because of enormous computational resource requirements. This paper proposes a lunar crater recognition system using binary neural networks (BNN) with low-computation resource hardware. An overview of the system is provided along with validation results. The proposed method was demonstrated to be effective in lunar crater recognition tasks on low-computation resource hardware, allowing the implementation of a CNN-based lightweight system in such an environment.