Evidence map›Paper›PMID 40269047›Full record

ArticleScientific reports2025

Research on object detection and recognition in remote sensing images based on YOLOv11.

Lu-Hao He, Yong-Zhang Zhou, Lei Liu, Wei Cao, Jian-Hua Ma

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

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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

5 authors.

Lu-Hao HeSchool of Earth Sciences and Engineering, Sun Yat-Sen University, Zhuhai, 519000, China.
Yong-Zhang ZhouSchool of Earth Sciences and Engineering, Sun Yat-Sen University, Zhuhai, 519000, China. zhouyz@mail.sysu.edu.cn.
Lei LiuSchool of Earth Sciences and Engineering, Sun Yat-Sen University, Zhuhai, 519000, China.
Wei CaoHunan Shizhuyuan Nonferrous Metals Co., Ltd., Chenzhou, 423000, China.
Jian-Hua MaSchool of Earth Sciences and Engineering, Sun Yat-Sen University, Zhuhai, 519000, China. 05161935@cumt.edu.cn.

Funding

Guangdong Key Areas Research and Development Project 2020B1111370001National Key Research and Development Plan 2022YFF0801201the National Natural Science Foundation of China U1911202
6 · The paper itself

Abstract

This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. The model was trained on 70,389 samples across 20 target categories. After 496 training epochs, the loss functions (Box_Loss, Cls_Loss, and DFL_Loss) demonstrated rapid convergence, indicating effective optimization in target localization, classification, and detail refinement. The evaluation metrics yielded a precision of 0.8861, a recall of 0.8563, a map

Indexed as

Computer visionDeep learningObject detectionRemote sensingYOLOv11

Identifiers

PMID40269047
PMCPMC12019343

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.