Trade‐off between number and length of remote videos for rapid assessments of reef fish assemblages

Garcia, Gabriel S.; Dias, Murilo S.; Longo, Guilherme O.

doi:10.1111/jfb.14776

Cited by 7 publications

(6 citation statements)

References 61 publications

(83 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is associated with excellent results even in low light, which represents a significant challenge in underwater environments. Extremely compact and affordable underwater video equipment in underwater housings has led to a significant increase in research possibilities involving scientific diving and testing of scientific hypotheses (Koenig and Stallings, 2015;Paula et al, 2018;Fontoura et al, 2020;Guabiroba et al, 2020;Garcia et al, 2021).…”

Section: Underwater Photography and Videography In Marine Sciencementioning

confidence: 99%

Scientific diving in Brazil: history, present and perspectives

Leite,

Pinheiro,

Berchez

et al. 2023

Ocean Coast. Res.

View full text Add to dashboard Cite

Section: Underwater Photography and Videography In Marine Sciencementioning

confidence: 99%

Scientific diving in Brazil: history, present and perspectives

Leite,

Pinheiro,

Berchez

et al. 2023

Ocean Coast. Res.

View full text Add to dashboard Cite

“…One way to reduce processing time is by subsampling videos to assess fish communities. Studies have looked at how the number of RUV samples affects the accuracy of data ( Garcia, Dias & Longo, 2021 ), and examined optimal soak times ( Asher, 2017 ; Wong et al, 2019 ; Garcia, Dias & Longo, 2021 ), but no studies have assessed precision and accuracy associated with subsampling fish metrics for RUVs.…”

Section: Introductionmentioning

confidence: 99%

Optimising sampling of fish assemblages on intertidal reefs using remote underwater video

Erickson

Bugnot

Figueira

2023

PeerJ

View full text Add to dashboard Cite

Background Assessing fish assemblages in subtidal and intertidal habitats is challenging due to the structural complexity of many of these systems. Trapping and collecting are regarded as optimal ways to sample these assemblages, but this method is costly and destructive, so researchers also use video techniques. Underwater visual census and baited remote underwater video stations are commonly used to characterise fish communities in these systems. More passive techniques such as remote underwater video (RUV) may be more appropriate for behavioural studies, or for comparing proximal habitats where the broad attraction caused by bait plumes could be an issue. However, data processing for RUVs can be time consuming and create processing bottlenecks. Methods Here, we identified the optimal subsampling method to assess fish assemblages on intertidal oyster reefs using RUV footage and bootstrapping techniques. We quantified how video subsampling effort and method (systematic vs random) affect the accuracy and precision of three different fish assemblage metrics; species richness and two proxies for the total abundance of fish, MaxNT and MeanCountT, which have not been evaluated previously for complex intertidal habitats. Results Results suggest that MaxNT and species richness should be recorded in real time, whereas optimal sampling for MeanCountT is every 60 s. Systematic sampling proved to be more accurate and precise than random sampling. This study provides valuable methodology recommendations which are relevant for the use of RUV to assess fish assemblages in a variety of shallow intertidal habitats.

show abstract

“…The real-time detection of fish is the key to realizing the protection of marine fish resources. Obtaining information such as the species and number of fish in an image or video can help assess the population and growth of fish, which is of great significance for maintaining the stability of the fish population [2]. However, it is unrealistic to rely on humans to manually analyze these images and video data, which is time-consuming, labor-intensive, and prone to errors.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Lightweight Model for the Detection of Marine Fishes

Wu,

Zhang,

Wang

et al. 2023

JMSE

View full text Add to dashboard Cite

The species and population size of marine fish are important for maintaining the ecological environment and reflecting climate change. Traditional fish detection methods mainly rely on manual or traditional computer vision, which has disadvantages such as complex design, low detection accuracy, and poor generalization. The widespread use of ocean observation systems has accumulated a large number of images and videos, which makes the application of deep learning on marine fish detection possible. In this paper, a real-time high-precision fish detection algorithm based on YOLOv5s is constructed. Considering the enhancement of the channel representation and spatial interaction ability of the model, the attention mechanism and gated convolution are introduced, respectively, and GhostNet is introduced to lighten the model. Through a series of model comparisons, two improved models, S-Head-Ghost-Fish9 and S-SE-HorBlock-Head-Ghost-Fish9, are finally obtained. Compared with the original model, in terms of model size, the former reduces by 19% and the latter increases by 9.5%; in terms of computation, the former reduces by 15.7% and the latter reduces by 3.1%; in terms of detection speed, both take about 17 ms to detect a single image, and both can meet the real-time detection requirements; in terms of detection accuracy, the former improves by 3% and the latter by 3.6%. Compared with the latest detection algorithms of YOLOv6 and YOLOv8, the detection accuracy is slightly lower than 1%, but the model size and computation amount are only 1/3 to 1/2 of them. The improved models can help assess the population size and growth of the fish, which is of great significance in maintaining the stability of the fish population.

show abstract

Trade‐off between number and length of remote videos for rapid assessments of reef fish assemblages

Cited by 7 publications

References 61 publications

Scientific diving in Brazil: history, present and perspectives

Scientific diving in Brazil: history, present and perspectives

Optimising sampling of fish assemblages on intertidal reefs using remote underwater video

A Deep Learning-Based Lightweight Model for the Detection of Marine Fishes

Contact Info

Product

Resources

About