Challenges in the deep learning-based semantic segmentation of benthic communities from Ortho-images

Abstract

Since the early days of the low-cost camera development, the collection of visual data has become a common practice in the underwater monitoring field. Nevertheless, video and image sequences are a trustworthy source of knowledge that remains partially untapped. Human-based image analysis is a time-consuming task that creates a bottleneck between data collection and extrapolation. Nowadays, the annotation of biologically meaningful information from imagery can be efficiently automated or accelerated by convolutional neural networks (CNN). Presenting our case studies, we offer an overview of the potentialities and difficulties of accurate automatic recognition and segmentation of benthic species. This paper focuses on the application of deep learning techniques to multi-view stereo reconstruction by-products (registered images, point clouds, ortho-projections), considering the proliferation of these techniques among the marine science community. Of particular importance is the need to semantically segment imagery in order to generate demographic data vital to understand and explore the changes happening within marine communities.

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    https://github.com/cnr-isti-vclab/TagLab

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Acknowledgments

Authors would like to thank the Sandin Lab (Scripps Institution of Oceanography, UCSD) for the collaboration and for kindly providing all the annotated orthos presented in this study. We thank Marco Callieri for his useful suggestions on how to improve the manuscript.

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Pavoni, G., Corsini, M., Pedersen, N. et al. Challenges in the deep learning-based semantic segmentation of benthic communities from Ortho-images. Appl Geomat 13, 131–146 (2021). https://doi.org/10.1007/s12518-020-00331-6

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Keywords

  • Underwater monitoring
  • Coral reef surveys
  • Semantic segmentation
  • Automatic classification
  • Deep Learning