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Метод распознавания камней на поверхности Марса с использованием сверточной сети с самовниманием

  • role： First author第一作者
  • Affiliation：

    College of Automation, Beijing Information Science and Technology University, Beijing 100192, China

    State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Researc Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：2023020486@bistu.edu.cn
  • Introduction：曹学欢， 研究方向为行星制图与遥感。E-mail： 2023020486@bistu.edu.cn
CAO Xuehuan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Researc Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：pengman@aircas.ac.cn
  • Introduction：彭嫚，研究方向为行星制图与遥感。E-mail： pengman@aircas.ac.cn
PENG Man2*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Researc Institute, Chinese Academy of Sciences, Beijing 100101, China

WAN Wenhui2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Researc Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

WANG Biao23,  
  • Affiliation：

    State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Researc Institute, Chinese Academy of Sciences, Beijing 100101, China

WANG Yexin2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Researc Institute, Chinese Academy of Sciences, Beijing 100101, China

DI Kaichang2,  
  • Affiliation：

    College of Automation, Beijing Information Science and Technology University, Beijing 100192, China

LI Lu1

реферат

Широкое распространение камней на поверхности Марса представляет потенциальную угрозу для безопасного передвижения марсоходов, одновременно характеристики распределения камней предоставляют важные подсказки для исследования геологической эволюции районов посадки на Марсе. Тем не менее, распознавание камней на изображениях марсоходов сталкивается с множеством проблем: размытые границы между камнями и фоном затрудняют выделение контуров, схожесть текстурных признаков поверхности приводит к ошибочным срабатываниям, а дефицит реальных марсианских наборов данных ограничивает обучение моделей. Для достижения точного распознавания камней на изображениях марсоходов в данной работе предложена автоматическая модель распознавания камней на основе сверточной сети с механизмом самовнимания, реализующая сегментацию изображения на уровне пикселей. Модель использует архитектуру энкодер-декодер, где энкодер основан на сверточной нейронной сети для извлечения признаков изображения и включает улучшенный модуль самовнимания для усиления восприятия контекстной информации; декодер восстанавливает признаки из пространства кодировки обратно в пространстве изображения для точной сегментации. Для проверки производительности модели проведена аннотация изображений марсохода "Чжужун" и построен датасет Tianwen, модель протестирована и валидирована на нескольких наборах данных, включая симулированные наборы камней Synmars, Simmars6k и реальный набор изображений марсохода Curiosity MarsData-v2. Кроме того, модель сравнивалась по точности с DeepLabv3+, Unet++, Segformer, Marsnet и другими методами. Оценка проводилась по среднему пиксельному точности, полноте и индексу пересечения по объединению (IoU), результаты показали, что модель способна точно распознавать камни, достигая более 90% по точности и полноте на симулированных наборах данных, а также показывая лучшие показатели на реальных наборах.

ключеви́че слова́

Марс; извлечение камней; сверточная нейронная сеть; трансформер; извлечение признаков

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