Evidence map›Paper›PMID 42162111›Full record

ArticleScientific reports2026

Efficient UAV object detection using spectro-spatial synergistic learning and implicit recursive refinement.

Xiaoji Wei, Zhongbin Luo, Bilu Luo, JunHao Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

4 authors.

Xiaoji WeiJiaxing Vocational and Technical College, Jiaxing, 314036, China.
Zhongbin LuoCollege of Computer Science, Chongqing University, Chongqing, 400044, China. luozhongbin@stu.cqu.edu.cn.
Bilu LuoJiaxing Vocational and Technical College, Jiaxing, 314036, China.
JunHao LiCollege of Computer and Information Science & College of Software, Southwest University, Chongqing, 400715, China.

Funding

General Scientific Research Project of Zhejiang Provincial Department of Education Y202456753Jiaxing Public Welfare Research Project 2025CGW075Jiaxing Team Science and Technology Commissioner Project K2024-1-1-06
6 · The paper itself

Abstract

Object detection in UAV imagery faces severe challenges arising from drastic scale variations, pervasive background noise, and the dominance of tiny objects, further compounded by the computational constraints of edge devices. Existing lightweight detectors frequently suffer from feature degradation, in which low-frequency background information tends to submerge the high-frequency details of small objects. To overcome this limitation, we propose S

Indexed as

Implicit recursive aggregationSpectro-spatial learningUAV small object detection

Identifiers

PMID42162111
PMCPMC13392264

What OpenQuestion holds

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Registered trials

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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.