What question did this study set out to answer?

To develop an affordable underwater camera for real-time monitoring of marine ecosystems using AI technology.

February 9, 2026Open Access

Development of low-cost underwater cameras for remote monitoring of marine ecosystems with ai-based species detection

Key Points

To develop an affordable underwater camera for real-time monitoring of marine ecosystems using AI technology.
Designed a modular underwater camera with a depth capability of 275 m.
Utilized a Raspberry Pi 5 and a 64 MP Arducam camera as key components.
Implemented YOLO-based AI software for species detection and monitoring.
Adapted power consumption and photo frequency based on environmental conditions.
Successfully created a low-cost camera that detects fish species underwater.
Demonstrated efficient operation both in connection with OBSEA and as a standalone unit.
Promoted sustainable marine conservation practices through enhanced monitoring capabilities.

Abstract

The low-cost camera, developed by the OBSEA team, is a modular, low-cost underwater camera capable of operating down to 275 m depth. It includes a Raspberry Pi 5, a 64 MP Arducam camera, LED lights and YOLO-based AI software to detect fish. It works connected to the OBSEA or on standalone platforms, adapting its power consumption and photo frequency according to the environment. It is an efficient and affordable solution that facilitates marine monitoring and promotes sustainable conservation.

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Authors

Pol Baños Castelló

Carlos de la Vega

Oriol Prat i Bayarri

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Development of low-cost underwater cameras for remote monitoring of marine ecosystems with ai-based species detection

Key Points

Abstract

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