用深度学习自动识别月球火星撞击坑，精度高且可扩展。
Deep learning framework for crater detection and identification on the Moon and Mars
- 两阶段框架：先用CNN/ResNet/YOLO初筛，再用YOLO精确定位
- YOLO在整体检测中表现最均衡，ResNet-50对大坑识别精度最高
- 适用于月球火星遥感数据，支持多类型撞击坑分析
撞击坑是行星表面最显著的地貌特征之一，在行星科学研究中具有重要意义。其空间分布与形态特征可反映地表成分、地质历史及撞击过程。近年来，深度学习模型的快速发展推动了自动撞击坑检测的研究。本文提出一种基于深度学习的撞击坑检测与识别框架，采用卷积神经网络（CNN）及其变体如YOLO和ResNet。该框架为两阶段设计：第一阶段使用经典CNN、ResNet-50和YOLO进行撞击坑初步识别；第二阶段则利用基于YOLO的检测方法实现撞击坑定位。通过月球和火星的遥感数据，我们对选定区域的撞击坑进行了检测与分类，并生成总结报告。结果表明，YOLO在整体检测性能上最为均衡，而ResNet-50在大撞击坑识别中表现出高精度。
原文摘要 · Abstract (English)
Impact craters are among the most prominent geomorphological features on planetary surfaces and are of substantial significance in planetary science research. Their spatial distribution and morphological characteristics provide critical information on planetary surface composition, geological history, and impact processes. In recent years, the rapid advancement of deep learning models has fostered significant interest in automated crater detection. In this paper, we apply advancements in deep learning models for impact crater detection and identification. We use novel models, including Convolutional Neural Networks (CNNs) and variants such as YOLO and ResNet. We present a framework that features a two-stage approach where the first stage features crater identification using simple classic CNN, ResNet-50 and YOLO. In the second stage, our framework employs YOLO-based detection for crater localisation. Therefore, we detect and identify different types of craters and present a summary report with remote sensing data for a selected region. We consider selected regions for craters and identification from Mars and the Moon based on remote sensing data. Our results indicate that YOLO demonstrates the most balanced crater detection performance, while ResNet-50 excels in identifying large craters with high precision.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。