KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection
Spaceborne infrared imagery provides substantial data support for ship detection due to its adaptability and all-weather reconnaissance capabilities. However, in complex maritime environments, its inadequate semantic information and feature distinctiveness increase the risk of false alarms and misse...
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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/11072708/ |
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| author | Yifei Peng Ze Tao Jian Zhang Rongchang Zhao Chao Liu Hui Sun |
| author_facet | Yifei Peng Ze Tao Jian Zhang Rongchang Zhao Chao Liu Hui Sun |
| author_sort | Yifei Peng |
| collection | DOAJ |
| description | Spaceborne infrared imagery provides substantial data support for ship detection due to its adaptability and all-weather reconnaissance capabilities. However, in complex maritime environments, its inadequate semantic information and feature distinctiveness increase the risk of false alarms and missed detections. To address these challenges, we propose a knowledge-driven feature fusion detection transformer (KDFF-DETR) for spaceborne infrared ship detection. KDFF-DETR presents an enhancement of RT-DETR. Specifically, we design a hybrid Fourier contextual (HFC) encoder to model the adequate semantic representation of high-level features through frequency-domain features and masked supervision. We further develop a visual knowledge-supervised feature reconstruction (VKFR) module, which introduces feature operators to represent visual knowledge and reconstruct handcrafted features to enhance their distinctiveness. Besides, a contribution-balanced feature fusion (CBFF) method for query selection is proposed, dynamically adjusting the weight distribution of handcrafted and deep features during different training stages to improve feature fusion accuracy and generalization ability. Experimental results indicate that KDFF-DETR surpasses state-of-the-art (SOTA) methods in various public datasets. |
| format | Article |
| id | doaj-art-6491b2fd57a141bbbd296767d8f721f6 |
| 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-6491b2fd57a141bbbd296767d8f721f62025-08-20T02:48:16ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118176381765310.1109/JSTARS.2025.358695511072708KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship DetectionYifei Peng0https://orcid.org/0009-0009-8951-0106Ze Tao1https://orcid.org/0000-0003-4009-194XJian Zhang2https://orcid.org/0000-0001-5418-0455Rongchang Zhao3https://orcid.org/0000-0002-5171-4121Chao Liu4Hui Sun5https://orcid.org/0009-0001-3713-1982School of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaInstitute of Systems Engineering, Academy of Military Science, Beijing, ChinaInstitute of Systems Engineering, Academy of Military Science, Beijing, ChinaSpaceborne infrared imagery provides substantial data support for ship detection due to its adaptability and all-weather reconnaissance capabilities. However, in complex maritime environments, its inadequate semantic information and feature distinctiveness increase the risk of false alarms and missed detections. To address these challenges, we propose a knowledge-driven feature fusion detection transformer (KDFF-DETR) for spaceborne infrared ship detection. KDFF-DETR presents an enhancement of RT-DETR. Specifically, we design a hybrid Fourier contextual (HFC) encoder to model the adequate semantic representation of high-level features through frequency-domain features and masked supervision. We further develop a visual knowledge-supervised feature reconstruction (VKFR) module, which introduces feature operators to represent visual knowledge and reconstruct handcrafted features to enhance their distinctiveness. Besides, a contribution-balanced feature fusion (CBFF) method for query selection is proposed, dynamically adjusting the weight distribution of handcrafted and deep features during different training stages to improve feature fusion accuracy and generalization ability. Experimental results indicate that KDFF-DETR surpasses state-of-the-art (SOTA) methods in various public datasets.https://ieeexplore.ieee.org/document/11072708/Contribution-balanced feature fusion (CBFF)handcrafted feature reconstructionhigh-level semantic modelingknowledge-driven detection transformer |
| spellingShingle | Yifei Peng Ze Tao Jian Zhang Rongchang Zhao Chao Liu Hui Sun KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Contribution-balanced feature fusion (CBFF) handcrafted feature reconstruction high-level semantic modeling knowledge-driven detection transformer |
| title | KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection |
| title_full | KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection |
| title_fullStr | KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection |
| title_full_unstemmed | KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection |
| title_short | KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection |
| title_sort | kdff detr knowledge driven feature fusion detr for spaceborne infrared ship detection |
| topic | Contribution-balanced feature fusion (CBFF) handcrafted feature reconstruction high-level semantic modeling knowledge-driven detection transformer |
| url | https://ieeexplore.ieee.org/document/11072708/ |
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