Evidence map›Paper›PMID 40670465›Full record

ArticleScientific reports2025

Research on lightweight tunnel cable fire recognition algorithm based on multi-scale features.

Zimeng Liu, Lei Zhang, Huiqiang Ma, Xuebing Chen, Molin Zhang

Abstract read
PubMed Full text at PMC Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Zimeng LiuCollege of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, 113001, Liaoning, China. ameng679@163.com.
Lei ZhangCollege of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, 113001, Liaoning, China.
Huiqiang MaCollege of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, 113001, Liaoning, China. 15898399040@126.com.
Xuebing ChenCollege of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, 113001, Liaoning, China.
Molin ZhangCollege of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, 113001, Liaoning, China.

Funding

Introduction of Talent Research Start-up Fund 1100130265the Basic Scientific Research Projects of Liaoning Province LJKMZ20220747
6 · The paper itself

Abstract

Currently, tunnel fire detection faces challenges such as slow response times, high false alarm rates, and poor timeliness. With the rapid development of computer vision, tunnel intelligent fire detection has received extensive attention from academia and industry. In this study, a lightweight YOLO-v5 tunnel cable fire recognition algorithm with multiscale features is proposed. By replacing the YOLO-v5 backbone network, Darknet53, with Mobilenetv3-small and integrating the SimAM attention mechanism, the lightweight and detection speed of the network was improved. Second, under the premise of retaining the feature fusion method of YOLOv5 splicing feature maps by channel, a Bi-directional Feature Pyramid Network (BiFPN) was constructed and the GIou_Loss function was introduced to enhance the network's target recognition precision. By designing tunnel cable fire experiments under different wind conditions to establish a image standard database, the accuracies and feasibilities of the model are verified. The training results show that the network's mean Average Precision (mAP 99%) improved by 0.4% and the FPS (179) improved by 46.7% compared to YOLOv5. The approach can meet the needs of tunnel cable fire detection on the precision and the speed. This method provides strong scientific and technological support for the improvement of tunnel Linear fire detection, facilitate the emergency management and therefore significantly contribute to loss prevention.

Indexed as

Cable fireDeep learningImage recognitionMulti-scale feature fusionYOLO-v5

Identifiers

PMID40670465
PMCPMC12267531

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.