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Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

Abstract : Coastal video monitoring has proven to be a valuable ground-based technique to investigate ocean processes. Presently, there is a growing need for automatic, technically efficient, and inexpensive solutions for image processing. Moreover, beach and coastal water quality problems are becoming significant and need attention. This study employs a methodological approach to exploit low-cost smartphone-based images for coastal image classification. The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring. A pixel-wise convolutional neural network (CNN) has demonstrated optimal performance in the classification of natural images by using abstracted deep features. Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management.
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Submitted on : Tuesday, January 7, 2020 - 10:15:26 AM
Last modification on : Wednesday, November 3, 2021 - 6:10:16 AM


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Nico Valentini, Yann Balouin. Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring. Journal of Marine Science and Engineering, MDPI, 2020, pp.1-23. ⟨10.3390/jmse8010023⟩. ⟨hal-02430104⟩



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