Applications of deep learning in fish habitat monitoring: A tutorial and survey

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

doi:10.1016/j.eswa.2023.121841

Cited by 9 publications

(3 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These resources are typically not provided by commercial companies that offer proprietary software and licenses. Data availability allows the user to further train the model based on their needs, leading to improved monitoring and deep learning models (Saleh et al., 2023 ). Hence, the methods described here align with modern perspectives on animal welfare, habitat protection and the transparency of open science.…”

Section: Discussionmentioning

confidence: 99%

“…Recent advances in the accessibility and relative ease of use of surveillance technology and deep learning models are shifting how ecological monitoring is conducted (Aguzzi et al., 2020 ; Saleh et al., 2020 , 2023 ). Surveillance cameras can produce copious amounts of video and image data, and deep learning models can analyse the data with speed and accuracy (Hentati‐Sundberg et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…CNNs have also found applications in the aquaculture industry for automatically counting the total number of fish in salmon and shrimp aquaculture. Furthermore, Saleh et al conducted a comprehensive survey of resource assessment research utilizing image recognition, summarizing the results in their survey study [10].…”

Section: Instance Segmentation + Keypoints and Error Detectionmentioning

confidence: 99%

Robust Fish Recognition Using Foundation Models toward Automatic Fish Resource Management

Hasegawa,

Nakano

2024

JMSE

View full text Add to dashboard Cite

Resource management for fisheries plays a pivotal role in fostering a sustainable fisheries industry. In Japan, resource surveys rely on manual measurements by staff, incurring high costs and limitations on the number of feasible measurements. This study endeavors to revolutionize resource surveys by implementing image-recognition technology. Our methodology involves developing a system that detects individual fish regions in images and automatically identifies crucial keypoints for accurate fish length measurements. We use grounded-segment-anything (Grounded-SAM), a foundation model for fish instance segmentation. Additionally, we employ a Mask Keypoint R-CNN trained on the fish image bank (FIB), which is an original dataset of fish images, to accurately detect significant fish keypoints. Diverse fish images were gathered for evaluation experiments, demonstrating the robust capabilities of the proposed method in accurately detecting both fish regions and keypoints.

show abstract

Marine Resources: Identification, Restoring, and Monitoring of Fisheries Food Resources Using Deep Learning and Image Processing

Ahamed,

Ayesha

2024

The Springer Series in Applied Machine Learning

View full text Add to dashboard Cite

Applications of deep learning in fish habitat monitoring: A tutorial and survey

Cited by 9 publications

References 73 publications

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

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

Robust Fish Recognition Using Foundation Models toward Automatic Fish Resource Management

Marine Resources: Identification, Restoring, and Monitoring of Fisheries Food Resources Using Deep Learning and Image Processing

Contact Info

Product

Resources

About