IDD-DETR: Insulator Defect Detection Model and Low-Carbon Operation and Maintenance Application Based on Bidirectional Cross-Scale Fusion and Dynamic Histogram Attention.

Weizhen Chen, Shuaishuai Li, Xingyu Han

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.

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Weizhen ChenSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, China.

Shuaishuai LiSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, China.ORCID 0009-0007-9000-4927

Xingyu HanSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, China.

Funding

Hubei Provincial Department of Education b2020061

6 · The paper itself

Abstract

Against the background of the "dual carbon" goal and the construction of a new power system, the intelligent operation and maintenance of insulators for ultra-high voltage transmission lines face challenges such as difficulty in detecting small-scale defects and strong interference from complex backgrounds. This paper proposes an improved network IDD-DETR to address the problems of inefficient one-way feature fusion and low-contrast defects that are easily overwhelmed in existing RT-DETR models. The enhanced network IDD-DETR replaces PAFPN with a Feature-Focused Diffusion Network (FFDN) and improves multi-scale fusion efficiency through bidirectional cross-scale interaction and designs Dynamic-Range Histogram Self-Attention (DHSA) to enhance defect response in low brightness areas. The experiment showed that its mAP

Indexed as

bidirectional feature fusiondefect detectiondynamic histogram attentionlow-carbon operation and maintenancetransmission line insulators

Identifiers

PMID41013086

DOI10.3390/s25185848

PMCPMC12473871

What OpenQuestion holds

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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.