Evidence map›Paper›PMID 37430900›Full record

ArticleSensors (Basel, Switzerland)2023

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition.

Alberto Monterroso Muñoz, Maria-Jose Moron-Fernández, Daniel Cascado-Caballero, Fernando Diaz-Del-Rio, Pedro Real

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 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. Review
  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

5 authors.

Alberto Monterroso MuñozDepartment of Applied Mathematics I, Universidad de Sevilla, 41012 Sevilla, Spain.ORCID 0000-0003-0790-4160
Maria-Jose Moron-FernándezDepartment of Computer Architecture and Technology, Universidad de Sevilla, 41012 Sevilla, Spain.ORCID 0000-0003-3291-7297
Daniel Cascado-CaballeroDepartment of Computer Architecture and Technology, Universidad de Sevilla, 41012 Sevilla, Spain.ORCID 0000-0002-6138-1009
Fernando Diaz-Del-RioDepartment of Computer Architecture and Technology, Universidad de Sevilla, 41012 Sevilla, Spain.ORCID 0000-0001-6184-1629
Pedro RealDepartment of Applied Mathematics I, Universidad de Sevilla, 41012 Sevilla, Spain.ORCID 0000-0002-6853-0505

Funding

European Regional Development Fund (FEDER) CIUCAP-HSF:US-1381077Ministry of Economy, Industry and Competitiveness PID2019-110455GB-I00
6 · The paper itself

Abstract

Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context.

Indexed as

image acquisitionimage processing methodsoptical imaging technologiessensing for autonomous underwater vehiclesunderwater image

Identifiers

PMID37430900
PMCPMC10222422

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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.