Evidence map›Paper›PMID 33436851›Full record

ArticleScientific reports2021

Domain randomization-enhanced deep learning models for bird detection.

Xin Mao, Jun Kang Chow, Pin Siang Tan, Kuan-Fu Liu, Jimmy Wu, Zhaoyu Su, Ye Hur Cheong, Ghee Leng Ooi, Chun Chiu Pang, Yu-Hsing Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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, 1 synthesis or guideline pooled it.

  1. Pooled it
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

10 authors.

Xin MaoDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Jun Kang ChowDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Pin Siang TanDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Kuan-Fu LiuDivision of Integrative Systems and Design, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Jimmy WuDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Zhaoyu SuDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Ye Hur CheongDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Ghee Leng OoiDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China.
Chun Chiu PangSchool of Biological Sciences, The University of Hong Kong, Hong Kong, SAR, China.
Yu-Hsing WangDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China. ceyhwang@ust.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

Indexed as

Deep LearningRandom AllocationAnimal MigrationAnimalsBirdsEnvironmentWeather

Identifiers

PMID33436851
PMCPMC7803967

What OpenQuestion holds

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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.