Computer vision and deep learning for fish classification in underwater habitats: A survey

Saleh, Alzayat; Sheaves, Marcus; Azghadi, Mostafa Rahimi

doi:10.1111/faf.12666

Cited by 55 publications

(44 citation statements)

References 144 publications

(195 reference statements)

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“…In future work, we will try to expand the scope of our proposed model by adding more datasets and tasks, such as underwater object tracking and counting, since the selfsupervised learning model can be viewed as a generic tool for underwater video processing, which is critical in underwater fish habitat monitoring [56] and the internet of underwater things [57].…”

Section: Discussionmentioning

confidence: 99%

Transformer-based Self-Supervised Fish Segmentation in Underwater Videos

Saleh¹,

Sheaves²,

Jerry³

et al. 2022

Preprint

Self Cite

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Underwater fish segmentation to estimate fish body measurements is still largely unsolved due to the complex underwater environment. Relying on fully-supervised segmentation models requires collecting per-pixel labels, which is timeconsuming and prone to overfitting. Self-supervised learning methods can help avoid the requirement of large annotated training datasets, however, to be useful in real-world applications, they should achieve good segmentation quality. In this paper, we introduce a Transformer-based method that uses self-supervision for high-quality fish segmentation. Our proposed model is trained on videos without any annotations to perform fish segmentation in underwater videos taken in situ in the wild. We show that when trained on a set of underwater videos from one dataset, the proposed model surpasses previous Convolutional Neural Network (CNN)-based and Transformer-based self-supervised methods and achieves performance relatively close to supervised methods on two new unseen underwater video datasets. This demonstrates the great generalisability of our model and the fact that it does not need a pre-trained model. In addition, we show that, due to its dense representation learning, our model is compute-efficient. We provide quantitative and qualitative results that demonstrate our model's significant capabilities.

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Section: Discussionmentioning

confidence: 99%

Transformer-based Self-Supervised Fish Segmentation in Underwater Videos

Saleh¹,

Sheaves²,

Jerry³

et al. 2022

Preprint

Self Cite

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“…Deep learning is a sub-field of machine learning composed of interrelated algorithms and concepts used in training a deep neural network (Saleh et al, 2022). One of the main reasons behind the extereme popularity of deep learning is the unprecedented and unparalleled performance it has achieved across different fields especially image recognition.…”

Section: Deep Learningmentioning

confidence: 99%

“…To learn deep features efficiently, researchers found that a modified version of neural networks, i.e. CNN, works very well in the image processing field (Saleh et al, 2022). In the following sections, we will first introduce the basic concepts of neural networks in general and then describe CNNs and explain how they learn and then process input images.…”

Section: Deep Learningmentioning

confidence: 99%

“…Although a number of previous relevant review articles (Goodwin et al, 2022;Li and Du, 2021;Zhao et al, 2021;Yang et al, 2021;Li et al, 2020;Moniruzzaman et al, 2017;Saleh et al, 2022) exist, our paper has a different approach and motivation that compliments prior surveys. Compared to (Goodwin et al, 2022), which provides a survey of the general domain of ecological data analysis, covering a wide array of studies on plankton, fish, marine mammals, pollution, and nutrient cycling, we focus only on fish monitoring.…”

Section: Introductionmentioning

confidence: 97%

“…In (Saleh et al, 2022), we have provided a historical survey of fish classification methods between the years 2003-2021. These methods cover traditional CV techniques and modern DL methods, only for fish classification in underwater habitats and not for the general domain of underwater fish habitat monitoring.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

Saleh¹,

Sheaves²,

Jerry³

et al. 2022

Preprint

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Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserving the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localization, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring.

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A hands‐on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams

Karlsson

2024

Ecology and Evolution

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This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. Furthermore, a comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. Access to video data and knowledge about deep learning models makes monitoring of trout and salmon dynamic and hands‐on, as the collected data can be used to train and further improve deep learning models. Hopefully, this setup will encourage fisheries managers to conduct more monitoring as the equipment is relatively cheap compared with customized solutions for fish monitoring. To make effective use of the data, natural markings of the camera‐captured fish can be used for individual identification. While the automated process greatly reduces the need for human intervention in video surveillance and speeds up the initial sorting and detection of fish, the manual identification of individual fish based on natural markings still requires human effort and involvement. Individual encounter data hold many potential applications, such as capture–recapture and relative abundance models, and for evaluating fish passages in streams with hydropower by spatial recaptures, that is, the same individual identified at different locations. There is much to gain by using this technique as camera captures are the better option for the fish's welfare and are less time‐consuming compared with physical captures and tagging.

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Computer vision and deep learning for fish classification in underwater habitats: A survey

Cited by 55 publications

References 144 publications

Transformer-based Self-Supervised Fish Segmentation in Underwater Videos

Transformer-based Self-Supervised Fish Segmentation in Underwater Videos

Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

A hands‐on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams

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