Evidence map›Paper›PMID 38139607›Full record

ArticleSensors (Basel, Switzerland)2023

Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR.

Min Zhao, Zehao Fang, Ning Ding, Nan Li, Tengfei Su, Huihuan Qian

Open access · goldAbstract 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
0.8field-weighted citation impact, top 31% of its field
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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

6 authors at 1 institution in 1 country.

Min ZhaoShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China.ORCID 0000-0003-1696-3277
Zehao FangShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China.
Ning DingShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China.
Nan LiShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China.ORCID 0000-0003-4407-934X
Tengfei SuShenzhen Water SCI&Tech. Development Co., Ltd., Shenzhen 518035, China.
Huihuan QianShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China.
Shenzhen Academy of Robotics · CN

Funding

National Key RD Program of China 2021YFE0204200Shenzhen Science and Technology Program JSGG20210802154539015
6 · The paper itself

Abstract

This paper introduces a novel method for enhancing underground pipeline inspection, specifically addressing limitations associated with traditional closed-circuit television (CCTV) systems. These systems, commonly used for capturing visual data of sewer system deformations, heavily rely on subjective human expertise, leading to limited accuracy in detection. Furthermore, their inability to perform quantitative analyses of deformation extent hampers overall inspection effectiveness. Our proposed method leverages laser point cloud data and employs a 3D scanner for objective detection of geometric deformations in underground pipe corridors. By utilizing this approach, we enable a quantitative assessment of blockage levels, offering a significant improvement over traditional CCTV-based methods. The key advantages of our method lie in its objectivity and quantification capabilities, ultimately enhancing detection reliability, accuracy, and overall inspection efficiency.

Indexed as

LiDARpipeline deformationpoint cloudquantitative detection

Identifiers

PMID38139607
PMCPMC10748281
OpenAlexW4389543288

What OpenQuestion holds

Textmetadata
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.