Overslaan en naar de inhoud gaan
Publicaties | Personen | Instituten | Projecten
[ meld een fout in dit record ]mandje (0): toevoegen | toon Print deze pagina

Seaweed growth monitoring with a low-cost vision-based system
Gerlo, J.; Kooijman, D.G.; Wieling, I.W.; Heirmans, R.; Vanlanduit, S. (2023). Seaweed growth monitoring with a low-cost vision-based system. Sensors 23(22): 9197. https://dx.doi.org/10.3390/s23229197
In: Sensors. MDPI: Basel. e-ISSN 1424-8220, meer
Peer reviewed article  

Beschikbaar in  Auteurs 

Trefwoord
    Marien/Kust
Author keywords
    aquaculture; seaweed monitoring; underwater stereo imaging; image segmentation

Auteurs  Top 
  • Gerlo, J., meer
  • Kooijman, D.G.
  • Wieling, I.W.
  • Heirmans, R., meer
  • Vanlanduit, S., meer

Abstract

    In this paper, we introduce a method for automated seaweed growth monitoring by combining a low-cost RGB and stereo vision camera. While current vision-based seaweed growth monitoring techniques focus on laboratory measurements or above-ground seaweed, we investigate the feasibility of the underwater imaging of a vertical seaweed farm. We use deep learning-based image segmentation (DeeplabV3+) to determine the size of the seaweed in pixels from recorded RGB images. We convert this pixel size to meters squared by using the distance information from the stereo camera. We demonstrate the performance of our monitoring system using measurements in a seaweed farm in the River Scheldt estuary (in The Netherlands). Notwithstanding the poor visibility of the seaweed in the images, we are able to segment the seaweed with an intersection of the union (IoU) of 0.9, and we reach a repeatability of 6% and a precision of the seaweed size of 18%.


Alle informatie in het Integrated Marine Information System (IMIS) valt onder het VLIZ Privacy beleid Top | Auteurs