C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory Prediction
Multidrone collaborative perception network can forecast the motion trajectories of grounded objects by aggregating intragroup communication and interaction, exhibiting significant potential across various applications. Existing collaborative perception methods struggle to address the nonuniform spa...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10883025/ |
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| _version_ | 1849709032139390976 |
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| author | Mingxin Chen Zhirui Wang Zhechao Wang Liangjin Zhao Peirui Cheng Hongqi Wang |
| author_facet | Mingxin Chen Zhirui Wang Zhechao Wang Liangjin Zhao Peirui Cheng Hongqi Wang |
| author_sort | Mingxin Chen |
| collection | DOAJ |
| description | Multidrone collaborative perception network can forecast the motion trajectories of grounded objects by aggregating intragroup communication and interaction, exhibiting significant potential across various applications. Existing collaborative perception methods struggle to address the nonuniform spatial distribution of targets and the spatial heterogeneity of multisource perception information typical in remote sensing scenarios. To tackle these challenges, we propose a coarse-to-fine feature fusion network C2F-Net, utilizing coarse-grained information interaction to guide the fusion of fine-grained features. Our approach includes a selective coarse-to-fine feature collaboration module that estimates perception levels of specific areas based on bird's-eye-view features, selectively collaborates on sparse features according to complementary information principles, and achieves efficient spatial feature interaction and fusion. In addition, we employ a region-aware effectiveness enhancement module, leveraging the differences between swarm and individual perception as prior knowledge to guide regional perception level estimation, improving comprehensive environmental understanding. We also introduce a simulation dataset named CoD-Pred for multidrone collaborative trajectory prediction. Extensive experiments demonstrate that C2F-Net significantly improves the accuracy of multidrone collaborative trajectory prediction, which increases mIoU by 2.7% to 3.3% and VPQ by 1.0% to 9.1% under comparable information transmission conditions, offering an effective and efficient solution for multidrone collaborative perception. |
| format | Article |
| id | doaj-art-0830bb6023b842d7a0ca555dc536fd18 |
| institution | DOAJ |
| 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-0830bb6023b842d7a0ca555dc536fd182025-08-20T03:15:27ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01186314632810.1109/JSTARS.2025.354124910883025C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory PredictionMingxin Chen0Zhirui Wang1https://orcid.org/0000-0003-2877-0384Zhechao Wang2https://orcid.org/0009-0003-9019-5031Liangjin Zhao3https://orcid.org/0000-0002-2590-591XPeirui Cheng4https://orcid.org/0000-0002-4993-6753Hongqi Wang5Aerospace Information Research Institute and the Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute and the Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaMultidrone collaborative perception network can forecast the motion trajectories of grounded objects by aggregating intragroup communication and interaction, exhibiting significant potential across various applications. Existing collaborative perception methods struggle to address the nonuniform spatial distribution of targets and the spatial heterogeneity of multisource perception information typical in remote sensing scenarios. To tackle these challenges, we propose a coarse-to-fine feature fusion network C2F-Net, utilizing coarse-grained information interaction to guide the fusion of fine-grained features. Our approach includes a selective coarse-to-fine feature collaboration module that estimates perception levels of specific areas based on bird's-eye-view features, selectively collaborates on sparse features according to complementary information principles, and achieves efficient spatial feature interaction and fusion. In addition, we employ a region-aware effectiveness enhancement module, leveraging the differences between swarm and individual perception as prior knowledge to guide regional perception level estimation, improving comprehensive environmental understanding. We also introduce a simulation dataset named CoD-Pred for multidrone collaborative trajectory prediction. Extensive experiments demonstrate that C2F-Net significantly improves the accuracy of multidrone collaborative trajectory prediction, which increases mIoU by 2.7% to 3.3% and VPQ by 1.0% to 9.1% under comparable information transmission conditions, offering an effective and efficient solution for multidrone collaborative perception.https://ieeexplore.ieee.org/document/10883025/Collaborative perceptiondrone swarmremote sensingtrajectory prediction |
| spellingShingle | Mingxin Chen Zhirui Wang Zhechao Wang Liangjin Zhao Peirui Cheng Hongqi Wang C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory Prediction IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Collaborative perception drone swarm remote sensing trajectory prediction |
| title | C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory Prediction |
| title_full | C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory Prediction |
| title_fullStr | C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory Prediction |
| title_full_unstemmed | C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory Prediction |
| title_short | C2F-Net: Coarse-to-Fine Multidrone Collaborative Perception Network for Object Trajectory Prediction |
| title_sort | c2f net coarse to fine multidrone collaborative perception network for object trajectory prediction |
| topic | Collaborative perception drone swarm remote sensing trajectory prediction |
| url | https://ieeexplore.ieee.org/document/10883025/ |
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