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<p>Impact craters are among the most prominent geomorphological features on planetary surfaces and are of substantial significance in planetary science research. Lately, the rapid advancement of deep learning models has fostered significant interest in automated crater detection. In this paper, we apply advanced deep learning models for impact crater detection and identification using Convolutional Neural Networks (CNNs) and their variants, including YOLO and ResNet. We present a framework that features a two-stage approach, where the first stage employs YOLO for crater detection and localisation. In the second stage, our framework features crater classification using CNN, ResNet and YOLO. 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 excels in identifying large craters with high precision. However, ResNet reported poor performance for large and medium craters for both Mars and the Moon, while CNN achieved the best performance for small craters on Mars.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月17日

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  • Yihan Ma,
  • Jessie Guo,
  • Zeyang Yu,
  • Rohitash Chandra

摘要

Impact craters are among the most prominent geomorphological features on planetary surfaces and are of substantial significance in planetary science research. Lately, the rapid advancement of deep learning models has fostered significant interest in automated crater detection. In this paper, we apply advanced deep learning models for impact crater detection and identification using Convolutional Neural Networks (CNNs) and their variants, including YOLO and ResNet. We present a framework that features a two-stage approach, where the first stage employs YOLO for crater detection and localisation. In the second stage, our framework features crater classification using CNN, ResNet and YOLO. 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 excels in identifying large craters with high precision. However, ResNet reported poor performance for large and medium craters for both Mars and the Moon, while CNN achieved the best performance for small craters on Mars.