Evidence map›Paper›PMID 38596081›Full record

ArticleHeliyon2024

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants.

JoonHyuk Lee, YoungSik Kim, Abdur Rehman, InKwon Kim, JaeJoon Lee, HongSik Yun

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. 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
1.5field-weighted citation impact, top 21% 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, 7 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 2 institutions in 1 country.

JoonHyuk LeeKorean Fire Protection Association, Seoul, 07328, South Korea.
YoungSik KimStratio, Inc., Seongnam-si, Gyeonggi-do, 13449, South Korea.
Abdur RehmanStratio, Inc., Seongnam-si, Gyeonggi-do, 13449, South Korea.
InKwon KimSound Camera Business/Software Lab., SM Instruments, Inc., Daejeon, 34109, South Korea.
JaeJoon LeeDepartment of Fire safety Engineering, Jeonju University, 303, Cheonjam-ro, Wansan-gu, Jeonju-si, Jeollabuk-do, 55069, South Korea.
HongSik YunInterdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, South Korea.
Jeonju University · KRSungkyunkwan University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP

Indexed as

Deep learningObject detectionPetrochemical plantPipe leakUltrasonic

Identifiers

PMID38596081
PMCPMC11002273
OpenAlexW4393344302

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.