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arXiv · 2009.09924

Multi-species Seagrass Detection and Classification from Underwater Images

Scarlett Raine·Ross Marchant·Peyman Moghadam·Frederic Maire·Brett Kettle·Brano Kusy
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Abstract

Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images. Manual review of these images to extract ecological data is prohibitive in terms of time and cost, thus providing strong incentive to automate this process using machine learning solutions. In this paper, we introduce a multi-species detector and classifier for seagrasses based on a deep convolutional neural network (achieved an overall accuracy of 92.4%). We also introduce a simple method to semi-automatically label image patches and therefore minimize manual labelling requirement. We describe and release publicly the dataset collected in this study as well as the code and pre-trained models to replicate our experiments at: https://github.com/csiro-robotics/deepseagrass

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BibTeXRIS

Scarlett Raine, Ross Marchant, Peyman Moghadam, Frederic Maire, Brett Kettle, Brano Kusy. 2020-09-18. Multi-species Seagrass Detection and Classification from Underwater Images. https://arxiv.org/abs/2009.09924

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