Preprints
https://doi.org/10.5194/esurf-2022-56
https://doi.org/10.5194/esurf-2022-56
 
14 Nov 2022
14 Nov 2022
Status: this preprint is currently under review for the journal ESurf.

Automated riverbed material analysis using Deep Learning on underwater images

Alexander Anatol Ermilov, Gergely Benkő, and Sándor Baranya Alexander Anatol Ermilov et al.
  • Department of Hydraulic and Water Resources Engineering, Budapest University of Technology and Economics, Budapest, 1111, Hungary

Abstract. The sediment of alluvial riverbeds plays a significant role in river systems both in engineering and natural processes. However, the sediment composition can show great spatial and temporal heterogeneity, even on river reach scale, making it difficult to representatively sample and assess. Indeed, conventional sampling methods in such cases cannot describe well the variability of the bed surface texture due to the amount of energy and time they would require. In this paper, an attempt is made to overcome this issue introducing a novel image-based, Deep Learning algorithm and related field measurement methodology with potential for becoming a complementary technique for bed material samplings and significantly reducing the necessary resources. The algorithm was trained to recognise main sediment classes in videos that were taken underwater in a large river with mixed bed sediments, along cross-sections, using semantic segmentation. The method is fast, i.e., the videos of 300–400 meter long sections can be analysed within minutes, with very dense spatial sampling distribution. The goodness of the trained algorithm is evaluated mathematically and via intercomparison with other direct and indirect methods. Suggestions for performing proper field measurements are also given, furthermore, possibilities for combining the algorithm with other techniques are highlighted, briefly showcasing the multi-purpose of underwater videos for hydromorphological adaptation. The paper is to show the potential of underwater videography and Deep Learning through a case study.

Alexander Anatol Ermilov et al.

Status: open (until 03 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Alexander Anatol Ermilov et al.

Alexander Anatol Ermilov et al.

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Short summary
A novel, artificial intelligence-based riverbed sediment analysis methodology is introduced, which uses underwater images to identify the characteristic sediment classes. The main novelties of the procedure are the followings: underwater images are used; the method enables continuous mapping of the riverbed along the measurement vessel’s route contrary to conventional techniques; cost-efficient; works without scaling.