AirFormer: Learning-Based Object Detection for Mars Helicopter
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 gre...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10746318/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850266393646202880 |
|---|---|
| author | Yifan Qi Xueming Xiao Meibao Yao Yonggang Xiong Lei Zhang Hutao Cui |
| author_facet | Yifan Qi Xueming Xiao Meibao Yao Yonggang Xiong Lei Zhang Hutao Cui |
| author_sort | Yifan Qi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e8ee5b776b094b898d120d7e3e70f3dc |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-e8ee5b776b094b898d120d7e3e70f3dc2025-08-20T01:54:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011810011110.1109/JSTARS.2024.349234610746318AirFormer: Learning-Based Object Detection for Mars HelicopterYifan Qi0Xueming Xiao1https://orcid.org/0000-0001-6511-0524Meibao Yao2https://orcid.org/0000-0002-7069-6782Yonggang Xiong3https://orcid.org/0000-0002-3058-4436Lei Zhang4Hutao Cui5CVIR lab, Changchun University of Science and Technology, Changchun, ChinaCVIR lab, Changchun University of Science and Technology, Changchun, ChinaSchool of Artificial Intelligence, Jilin University, Changchun, ChinaShanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, ChinaKey Lab of Opto-electronic Measurement and Optical Information Transmission Technology, Ministry of Education, Changchun, ChinaSchool of Astronautics, Harbin Institute of Technology, Harbin, ChinaIn 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.https://ieeexplore.ieee.org/document/10746318/Mars datasetMars explorationobject detectiontransformer model |
| spellingShingle | Yifan Qi Xueming Xiao Meibao Yao Yonggang Xiong Lei Zhang Hutao Cui AirFormer: Learning-Based Object Detection for Mars Helicopter IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Mars dataset Mars exploration object detection transformer model |
| title | AirFormer: Learning-Based Object Detection for Mars Helicopter |
| title_full | AirFormer: Learning-Based Object Detection for Mars Helicopter |
| title_fullStr | AirFormer: Learning-Based Object Detection for Mars Helicopter |
| title_full_unstemmed | AirFormer: Learning-Based Object Detection for Mars Helicopter |
| title_short | AirFormer: Learning-Based Object Detection for Mars Helicopter |
| title_sort | airformer learning based object detection for mars helicopter |
| topic | Mars dataset Mars exploration object detection transformer model |
| url | https://ieeexplore.ieee.org/document/10746318/ |
| work_keys_str_mv | AT yifanqi airformerlearningbasedobjectdetectionformarshelicopter AT xuemingxiao airformerlearningbasedobjectdetectionformarshelicopter AT meibaoyao airformerlearningbasedobjectdetectionformarshelicopter AT yonggangxiong airformerlearningbasedobjectdetectionformarshelicopter AT leizhang airformerlearningbasedobjectdetectionformarshelicopter AT hutaocui airformerlearningbasedobjectdetectionformarshelicopter |