SDRFPT-Net: A Spectral Dual-Stream Recursive Fusion Network for Multispectral Object Detection Article Swipe
Pu Zhou
,
Xiaoyong Sun
,
Bei Sun
,
Runze Guo
,
Zhaoyang Dang
,
Shaojing Su
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17132312
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17132312
Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Target Network), a novel architecture that integrates three key innovative modules: (1) the Spectral Hierarchical Perception Architecture (SHPA), which adopts a dual-stream separated structure with independently parameterized feature extraction paths for visible and infrared modalities; (2) the Spectral Recursive Fusion Module (SRFM), which combines hybrid attention mechanisms with recursive progressive fusion strategies to achieve deep feature interaction through parameter-sharing multi-round recursive processing; and (3) the Spectral Target Perception Enhancement Module (STPEM), which adaptively enhances target region representation and suppresses background interference. Extensive experiments on the VEDAI, FLIR-aligned, and LLVIP datasets demonstrate that SDRFPT-Net significantly outperforms state-of-the-art methods, with improvements of 2.5% in mAP50 and 5.4% in mAP50:95 on VEDAI, 11.5% in mAP50 on FLIR-aligned, and 9.5% in mAP50:95 on LLVIP. Ablation studies further validate the effectiveness of each proposed module. The proposed method provides an efficient and robust solution for multispectral object detection in remote sensing image interpretation, making it particularly suitable for all-weather monitoring applications from aerial and satellite platforms, as well as in intelligent surveillance and autonomous driving domains.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17132312
- https://www.mdpi.com/2072-4292/17/13/2312/pdf?version=1751708747
- OA Status
- gold
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412067411
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4412067411Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17132312Digital Object Identifier
- Title
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SDRFPT-Net: A Spectral Dual-Stream Recursive Fusion Network for Multispectral Object DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-05Full publication date if available
- Authors
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Pu Zhou, Xiaoyong Sun, Bei Sun, Runze Guo, Zhaoyang Dang, Shaojing SuList of authors in order
- Landing page
-
https://doi.org/10.3390/rs17132312Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/17/13/2312/pdf?version=1751708747Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/17/13/2312/pdf?version=1751708747Direct OA link when available
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Dual (grammatical number), Multispectral image, Computer science, Net (polyhedron), Object (grammar), Fusion, Remote sensing, Artificial intelligence, Computer vision, Geology, Mathematics, Geometry, Art, Linguistics, Literature, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-07-05 |
| publication_year | 2025 |
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| abstract_inverted_index.as | 187, 189 |
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| abstract_inverted_index.it | 175 |
| abstract_inverted_index.of | 125, 152 |
| abstract_inverted_index.on | 109, 133, 138, 144 |
| abstract_inverted_index.to | 78 |
| abstract_inverted_index.(1) | 37 |
| abstract_inverted_index.(2) | 61 |
| abstract_inverted_index.(3) | 89 |
| abstract_inverted_index.The | 156 |
| abstract_inverted_index.and | 58, 88, 103, 113, 129, 140, 162, 184, 193 |
| abstract_inverted_index.for | 56, 165, 178 |
| abstract_inverted_index.key | 34 |
| abstract_inverted_index.the | 38, 62, 90, 110, 150 |
| abstract_inverted_index.2.5% | 126 |
| abstract_inverted_index.5.4% | 130 |
| abstract_inverted_index.9.5% | 141 |
| abstract_inverted_index.This | 17 |
| abstract_inverted_index.deep | 80 |
| abstract_inverted_index.each | 153 |
| abstract_inverted_index.from | 10, 182 |
| abstract_inverted_index.that | 31, 117 |
| abstract_inverted_index.well | 188 |
| abstract_inverted_index.with | 50, 73, 123 |
| abstract_inverted_index.11.5% | 135 |
| abstract_inverted_index.LLVIP | 114 |
| abstract_inverted_index.faces | 3 |
| abstract_inverted_index.image | 172 |
| abstract_inverted_index.mAP50 | 128, 137 |
| abstract_inverted_index.novel | 29 |
| abstract_inverted_index.paper | 18 |
| abstract_inverted_index.paths | 55 |
| abstract_inverted_index.three | 33 |
| abstract_inverted_index.which | 44, 68, 97 |
| abstract_inverted_index.Fusion | 24, 65 |
| abstract_inverted_index.LLVIP. | 145 |
| abstract_inverted_index.Module | 66, 95 |
| abstract_inverted_index.Target | 26, 92 |
| abstract_inverted_index.VEDAI, | 111, 134 |
| abstract_inverted_index.adopts | 45 |
| abstract_inverted_index.aerial | 183 |
| abstract_inverted_index.fusion | 76 |
| abstract_inverted_index.hybrid | 70 |
| abstract_inverted_index.making | 174 |
| abstract_inverted_index.method | 158 |
| abstract_inverted_index.object | 1, 167 |
| abstract_inverted_index.region | 101 |
| abstract_inverted_index.remote | 170 |
| abstract_inverted_index.robust | 163 |
| abstract_inverted_index.target | 100 |
| abstract_inverted_index.(SHPA), | 43 |
| abstract_inverted_index.(SRFM), | 67 |
| abstract_inverted_index.achieve | 79 |
| abstract_inverted_index.complex | 14 |
| abstract_inverted_index.driving | 195 |
| abstract_inverted_index.feature | 53, 81 |
| abstract_inverted_index.further | 148 |
| abstract_inverted_index.module. | 155 |
| abstract_inverted_index.sensing | 171 |
| abstract_inverted_index.studies | 147 |
| abstract_inverted_index.through | 83 |
| abstract_inverted_index.visible | 57 |
| abstract_inverted_index.(STPEM), | 96 |
| abstract_inverted_index.Ablation | 146 |
| abstract_inverted_index.Spectral | 39, 63, 91 |
| abstract_inverted_index.combines | 69 |
| abstract_inverted_index.datasets | 115 |
| abstract_inverted_index.domains. | 196 |
| abstract_inverted_index.enhances | 99 |
| abstract_inverted_index.infrared | 59 |
| abstract_inverted_index.mAP50:95 | 132, 143 |
| abstract_inverted_index.methods, | 122 |
| abstract_inverted_index.modules: | 36 |
| abstract_inverted_index.proposed | 154, 157 |
| abstract_inverted_index.proposes | 19 |
| abstract_inverted_index.provides | 159 |
| abstract_inverted_index.solution | 164 |
| abstract_inverted_index.suitable | 177 |
| abstract_inverted_index.validate | 149 |
| abstract_inverted_index.(Spectral | 21 |
| abstract_inverted_index.Extensive | 107 |
| abstract_inverted_index.Network), | 27 |
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| abstract_inverted_index.attention | 71 |
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| abstract_inverted_index.structure | 49 |
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| abstract_inverted_index.autonomous | 194 |
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| abstract_inverted_index.platforms, | 186 |
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| abstract_inverted_index.Dual-stream | 22 |
| abstract_inverted_index.Enhancement | 94 |
| abstract_inverted_index.all-weather | 179 |
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| abstract_inverted_index.effectively | 6 |
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| abstract_inverted_index.representation | 102 |
| abstract_inverted_index.interpretation, | 173 |
| abstract_inverted_index.state-of-the-art | 121 |
| abstract_inverted_index.parameter-sharing | 84 |
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| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I170215575 |
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |