Mitigating Spectral Imbalance and Detail Attenuation in RGB-Thermal Object Detection via Frequency-Guided Multimodal Fusion.

Quan Du, Ming Zhao, Lu Song, Minnan Hu, Zhengqiang Wang, Wangyu Wu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Quan DuSchool of Computer Science, Yangtze University, Jingzhou 434023, China.ORCID 0009-0000-7216-9399

Ming ZhaoSchool of Computer Science, Yangtze University, Jingzhou 434023, China.ORCID 0000-0002-1647-1769

Lu SongSchool of Cyber Science and Engineering, Wuxi University, Wuxi 214015, China.

Minnan HuSchool of Cyber Science and Engineering, Wuxi University, Wuxi 214015, China.

Zhengqiang WangSchool of Physics & Electronic Engineering, Hubei University of Arts and Science, Xiangyang 441053, China.

Wangyu WuSchool of Computer Science, University of Liverpool, Liverpool L69 3DR, UK.ORCID 0009-0005-8404-9489

Funding

Wuxi University 2025r023

6 · The paper itself

Abstract

RGB-T object detection combines visible texture information with thermal saliency cues to improve detection under degraded illumination. Existing RGB-T fusion methods usually perform feature interaction in the spatial domain or treat spectral responses jointly, which may allow coarse background components to dominate the fusion process while weakening boundary and small-target details. In addition, the repeated upsampling and aggregation operations in the detection neck can further smooth high-frequency responses preserved during early fusion. This paper proposes F

Indexed as

cross-modal alignmentfeature reconstructionfrequency-domain learningmultispectral detectionRGB-T

Identifiers

PMID42451388

DOI10.3390/s26134145

PMCPMC13364440

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.