Evidence map›Paper›PMID 38202865›Full record

ArticleSensors (Basel, Switzerland)2023

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation.

Zhicheng Zhang, Zhijing Shen, Jintong Liu, Jiangpeng Shu, He Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zhicheng ZhangCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Zhijing ShenCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Jintong LiuCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Jiangpeng ShuCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
He ZhangCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0003-2168-0503

Funding

Foundation for Distinguished Young Scientists of Zhejiang Province LR20E080003the National Key R&D Program of China 2020YFA0711700the National Natural Science Foundation of China 11925206the National Natural Science Foundation of China 51978609the National Natural Science Foundation of China 52122801
6 · The paper itself

Abstract

The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder-decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from -3.9% to 36.0%, indicating the practical feasibility of our proposed method.

Indexed as

binocular visioncrack widthdeep learningimage processingnon-contact measurement

Identifiers

PMID38202865
PMCPMC10780333

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