Inspection of Underwater Hull Surface Condition Using the Soft Voting Ensemble of the Transfer-Learned Models.

Byung Chul Kim, Hoe Chang Kim, Sungho Han, Dong Kyou Park

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 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

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.

Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing.Sensors (Basel, Switzerland) · 2022
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

4 authors.

Byung Chul KimSchool of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea.ORCID 0000-0001-7073-5318

Hoe Chang KimSchool of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea.

Sungho HanSLM Global Co., Ltd., Daejeon 34037, Korea.

Dong Kyou ParkDepartment of Electromechanical Convergence Engineering, Korea University of Technology and Education, Cheonan 31253, Korea.

Funding

Korea Evaluation Institute of Industrial Technology 20012218Korea Evaluation Institute of Industrial Technology RS-2022-00143813National Research Foundation of Korea 2021RIS-004National Research Foundation of Korea NRF-2020R1I1A3066259

6 · The paper itself

Abstract

In this study, we propose a method for inspecting the condition of hull surfaces using underwater images acquired from the camera of a remotely controlled underwater vehicle (ROUV). To this end, a soft voting ensemble classifier comprising six well-known convolutional neural network models was used. Using the transfer learning technique, the images of the hull surfaces were used to retrain the six models. The proposed method exhibited an accuracy of 98.13%, a precision of 98.73%, a recall of 97.50%, and an F

Indexed as

LearningNeural Networks, Computerhull cleaning conditionsoft voting ensemble classificationtransfer learningunderwater inspection image

Identifiers

PMID35746174

DOI10.3390/s22124392

PMCPMC9231155

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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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.