Deep learning framework for crater detection and identification on the Moon and Mars

TL;DR

This paper presents a deep learning framework utilizing CNN, ResNet, and YOLO models for automated detection and identification of impact craters on the Moon and Mars, aiding planetary surface analysis.

Contribution

It introduces a two-stage deep learning approach combining classic CNN, ResNet-50, and YOLO for improved crater detection and classification on planetary remote sensing data.

Findings

YOLO provides balanced detection performance.

ResNet-50 excels in identifying large craters.

Framework applied to lunar and Martian remote sensing data.

Abstract

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.