Multi-species Seagrass Detection and Classification from Underwater Images

September 18, 2020 · Entered Twilight · 🏛 International Conference on Digital Image Computing: Techniques and Applications

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Repo contents: LICENSE.txt, README.md, data_loader.py, images, inference.py, inference_289x260.py, requirements.txt, train.py

Authors Scarlett Raine, Ross Marchant, Peyman Moghadam, Frederic Maire, Brett Kettle, Brano Kusy arXiv ID 2009.09924 Category cs.CV: Computer Vision Cross-listed cs.LG, eess.IV Citations 22 Venue International Conference on Digital Image Computing: Techniques and Applications Repository https://github.com/csiro-robotics/deepseagrass ⭐ 11 Last Checked 1 month ago

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