Evidence map›Paper›PMID 38894066›Full record

ArticleSensors (Basel, Switzerland)2024

Image-Based Bolt-Loosening Detection Using a Checkerboard Perspective Correction Method.

Chengqian Xie, Jun Luo, Kaili Li, Zhitao Yan, Feng Li, Xiaogang Jia, Yuanlai Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Chengqian XieSchool of Civil Engineering and Architecture, Chongqing University of Science and Technology, No. 20, East University Town Road, Shapingba District, Chongqing 401331, China.ORCID 0000-0003-2742-7250
Jun LuoSchool of Civil Engineering and Architecture, Chongqing University of Science and Technology, No. 20, East University Town Road, Shapingba District, Chongqing 401331, China.
Kaili LiSchool of Civil Engineering and Architecture, Chongqing University of Science and Technology, No. 20, East University Town Road, Shapingba District, Chongqing 401331, China.
Zhitao YanSchool of Civil Engineering and Architecture, Chongqing University of Science and Technology, No. 20, East University Town Road, Shapingba District, Chongqing 401331, China.ORCID 0000-0003-0148-8944
Feng LiChongqing Urban Investment Infrastructure Construction Co., Ltd., Chongqing 400015, China.
Xiaogang JiaChongqing Urban Investment Infrastructure Construction Co., Ltd., Chongqing 400015, China.
Yuanlai WangChongqing Urban Investment Infrastructure Construction Co., Ltd., Chongqing 400015, China.

Funding

Chongqing Urban Investment Infrastructure Construction Co., Ltd Program CQCT-JS-SC-GC-2022-0079Natural Science Foundation of Chongqing CSTB2023NSCQ-LZX0051Research Foundation of Chongqing University of Science and Technology ckrc2019033
6 · The paper itself

Abstract

In this paper, a new image-correction method for flange joint bolts is proposed. A checkerboard is arranged on the side of a flange node bolt, and the homography matrix can be estimated using more than four feature points, which include the checkerboard corner points. Then, the perspective distortion of the captured image and the deviation of the camera position angle are corrected using the estimated homography matrix. Due to the use of more feature points, the stability of homography matrix identification is effectively improved. Simultaneously, the influence of the number of feature points, camera lens distance, and light intensities are analyzed. Finally, based on a bolt image taken using an iPhone 12, the prototype structure of the flange joint in the laboratory is verified. The results show that the proposed method can effectively correct image distortion and camera position angle deviation. The use of more than four correction points not only effectively improves the stability of bolt image correction but also improves the stability and accuracy of bolt-loosening detection. The analysis of influencing factors shows that the proposed method is still effective when the number of checkerboard correction points is reduced to nine, and the average error of the bolt-loosening detection result is less than 1.5 degrees. Moreover, the recommended camera shooting distance range is 20 cm to 60 cm, and the method exhibits low sensitivity to light intensity.

Indexed as

bolt looseningcamera calibrationcheckerboardflange jointsperspective correction

Identifiers

PMID38894066
PMCPMC11174449

What OpenQuestion holds

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LicenceCC BY
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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.