Enhancing species video detection capabilities at the obsea observatory through the integration of emuas cameras within the aneris project framework

Abstract

High-resolution images captured by EMUAS cameras, equipped with a 4K sensor and set to 1440p resolution for the OBSEA deployment, are analysed using the YOLO (You Only Look Once) object detection algorithm, trained with labelled datasets from OBSEA. The cameras operate at 20 frames per second (fps) with H.264+ encoding and a maximum bitrate of 16384. The machine learning model used efficiently identifies and classifies up to 24 marine species. By leveraging convolutional neural networks, the system provides accurate and real-time species recognition, supporting biodiversity assessments and facilitating data-driven marine conservation efforts.