Evidence map›Paper›PMID 35808378›Full record

ArticleSensors (Basel, Switzerland)2022

Insulator Umbrella Disc Shedding Detection in Foggy Weather.

Rui Xin, Xi Chen, Junying Wu, Ke Yang, Xinying Wang, Yongjie Zhai

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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

6 authors.

Rui XinState Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050013, China.
Xi ChenState Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050013, China.
Junying WuState Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050013, China.ORCID 0000-0001-5501-7115
Ke YangDepartment of Automation, North China Electric Power University, Baoding 071003, China.ORCID 0000-0001-7109-9173
Xinying WangDepartment of Automation, North China Electric Power University, Baoding 071003, China.ORCID 0000-0001-5677-197X
Yongjie ZhaiDepartment of Automation, North China Electric Power University, Baoding 071003, China.ORCID 0000-0003-2997-5840

Funding

National Natural Science Foundation of China U21A20486Science and Technology Project of State Grid Hebei Information and telecommunication Branch No.SGHEXT00SJJS2100036
6 · The paper itself

Abstract

The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network's attention to the details of small targets and improve the model's ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively.

Indexed as

AlgorithmsWeatherData CollectionResearch Designattention mechanismdefect detectiondehazing algorithmfeature fusioninsulator umbrella disc shedding

Identifiers

PMID35808378
PMCPMC9269560

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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.