Evidence map›Paper›PMID 40648407›Full record

ArticleSensors (Basel, Switzerland)2025

MRC-DETR: A High-Precision Detection Model for Electrical Equipment Protection in Power Operations.

Shenwang Li, Yuyang Zhou, Minjie Wang, Li Liu, Thomas Wu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

5 authors.

Shenwang LiSchool of Electrical Engineering, Guangxi University, Nanning 530004, China.
Yuyang ZhouSchool of Electrical Engineering, Guangxi University, Nanning 530004, China.
Minjie WangSchool of Electrical Engineering, Guangxi University, Nanning 530004, China.
Li LiuSchool of Electrical Engineering, Guangxi University, Nanning 530004, China.
Thomas WuSchool of Electrical Engineering, Guangxi University, Nanning 530004, China.

Funding

Guangxi Science and Technology Major Program AA23073019
6 · The paper itself

Abstract

Ensuring that electrical workers use personal protective equipment (PPE) correctly is critical to electrical safety, but existing detection methods face significant limitations when applied in the electrical industry. This paper introduces MRC-DETR (Multi-Scale Re-calibration Detection Transformer), a novel framework for detecting Power Engineering Personal Protective Equipment (PEPPE) in complex electrical operating environments. Our method introduces two technical innovations: a Multi-Scale Enhanced Boundary Attention (MEBA) module, which significantly improves the detection of small and occluded targets through optimized feature representation, and a knowledge distillation strategy that enables efficient deployment on edge devices. We further contribute a dedicated PEPPE dataset to address the lack of domain-specific training data. Experimental results demonstrate superior performance compared to existing methods, particularly in challenging power industry scenarios.

Indexed as

deep learningDETRmodel deploymentPower Engineering Personal Protective Equipment (PEPPE)real-time object detection

Identifiers

PMID40648407
PMCPMC12251792

What OpenQuestion holds

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LicenceCC BY
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.