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AirFormer: Learning-Based Object Detection for Mars Helicopter

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  • NETL
  • NSTL
  • IEEE
In future multiagent Mars detection schemes, the Mars helicopter can assist the scientific missions of Mars rovers by providing navigation information and scientific objects. However, Mars surface exhibits a complex topography with diverse objects and similar textures to the background, posing a great challenge for existing CNN-based object detection networks. In this article, we propose a novel deep learning-based object detection framework, AirFormer, for Mars helicopter. AirFormer embeds a new feature-fusion attention module, MAT, which injects various receptive field sizes into labels. This fusion module is capable of capturing the interrelations between objects with each other while simultaneously reducing computational complexity. In addition, we published a synthetic dataset from the viewpoint of the Mars helicopter: SynMars-Air, which refers to the data collected by the ZhuRong rover. Extensive experiments are conducted to validate the performance of AirFormer compared to SOTA methods. The results show that our method achieved the highest accuracy both on synthetic and real Mars landscapes.

MarsObject detectionFeature extractionHelicoptersRocksFusesAtmospheric modelingSpace vehiclesSemantic segmentationRemote sensing

Yifan Qi、Xueming Xiao、Meibao Yao、Yonggang Xiong、Lei Zhang、Hutao Cui

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CVIR lab, Changchun University of Science and Technology, Changchun, China

School of Artificial Intelligence, Jilin University, Changchun, China|Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, Changchun, China

Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China

Key Lab of Opto-electronic Measurement and Optical Information Transmission Technology, Ministry of Education, Changchun, China

School of Astronautics, Harbin Institute of Technology, Harbin, China

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2025

10.1109/JSTARS.2024.3492346

IEEE journal of selected topics in applied earth observations and remote sensing

IEEE journal of selected topics in applied earth observations and remote sensing

ISSN：
年,卷(期)：2025.18(1)
  • 62