Evidence map›Paper›PMID 35684729›Full record

ArticleSensors (Basel, Switzerland)2022

Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures.

Voncarlos M Araújo, Ankita Shukla, Clément Chion, Sébastien Gambs, Robert Michaud

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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Voncarlos M AraújoDépartement des Sciences Naturelles, Université du Québec en Outaouais, Ripon, QC J0V 1V0, Canada.ORCID 0000-0002-7103-6882
Ankita ShuklaSchool of Arts, Media and Engineering, Arizona State University, Tempe, AZ 85281, USA.
Clément ChionDépartement des Sciences Naturelles, Université du Québec en Outaouais, Ripon, QC J0V 1V0, Canada.
Sébastien GambsDépartement d'Informatique, Université du Québec à Montréal (UQAM), Montreal, QC H2L 2C4, Canada.
Robert MichaudGroupe de Recherche et d'Éducation sur les Mammifères Marins (GREMM), Tadoussac, QC G0T 2A0, Canada.

Funding

Réseau Québec Maritime (RQM) - Programme Odyssée 2017-2022-39557
6 · The paper itself

Abstract

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (

Indexed as

Beluga WhaleAnimalsEcosystemHumansMachine LearningShipsautomatic object detectionbeluga whale monitoringdeep learningocean protection

Identifiers

PMID35684729
PMCPMC9185326

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.