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Transactions on Maritime Science, Vol. 14 No. 1, 2025.

Original scientific paper

https://doi.org/10.7225/toms.v14.n01.002

Real-Time Epizootic Monitoring with Inception Deep Neural Network for Maritime Applications

Denis Krivoguz orcid id orcid.org/0000-0002-7368-3303 ; Southern Federal University, Rostov-on-Don, Russia
Alexander Ioshpa orcid id orcid.org/0000-0003-4573-4393 ; Southern Federal University, Rostov-on-Don, Russia
Sergei Chernyi orcid id orcid.org/0000-0001-5702-3260 ; St. Petersburg State Marine Technical University, St. Petersburg, Russia *
Anton Zhilenkov ; St. Petersburg State Marine Technical University, St. Petersburg, Russia
Aleksandr Kustov ; St. Petersburg State Marine Technical University, St. Petersburg, Russia
Ilya Moiseev ; St. Petersburg State Marine Technical University, St. Petersburg, Russia
Mikhail Serebryakov ; St. Petersburg State Marine Technical University, St. Petersburg, Russia
Tatiana Kaynova ; St. Petersburg State Marine Technical University, St. Petersburg, Russia
Dmitry Vorontsov ; St. Petersburg State Marine Technical University, St. Petersburg, Russia
Kristina Gritsenko ; St. Petersburg State Marine Technical University, St. Petersburg, Russia

* Corresponding author.

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Abstract

This study explores the integration of artificial intelligence in aquaculture to differentiate healthy fish from those afflicted with diseases, aiming to establish a real-time, automated epizootic monitoring system. Utilizing the “Inception v3” convolutional neural network, we examined the model's efficacy in classifying fish based on their health status across two experiments focusing on data augmentation variability. Initial results without augmentation showed diseased fish detection with 86.7% accuracy and healthy fish detection with 86.9% accuracy. However, employing diverse augmentation techniques significantly enhanced detection accuracy to 96.9% for diseased fish and 96.7% for healthy specimens. These findings underscore the potential of computer vision technologies in revolutionizing epizootic monitoring in aquaculture by providing a non-invasive, accurate, and scalable solution to fish health management. The successful application of AI in this context could significantly contribute to the sustainability and productivity of aquaculture operations, underscoring a pivotal shift towards more advanced and humane practices in the industry.

Keywords

Computer vision; Epizootic monitoring; Deep learning; Artificial intelligence; Fish detection; Fish diseases

Hrčak ID:

330296

URI

https://hrcak.srce.hr/330296

Publication date:

20.4.2025.

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