Over het archief
Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
FishKeypoints: A comprehensive stereo vision framework for fish behaviour monitoring and size measurement in aquaculture
Zhang, Q.; Liu, D.; Parmiggiani, A.; Norton, T.; Fore, M.; Kelasidi, E. (2026). FishKeypoints: A comprehensive stereo vision framework for fish behaviour monitoring and size measurement in aquaculture. Comput. Electron. Agric. 250: 111852. https://dx.doi.org/10.1016/j.compag.2026.111852
In: Computers and Electronics in Agriculture. Elsevier: Amsterdam. ISSN 0168-1699; e-ISSN 1872-7107, meer
|  |
| Author keywords |
Stereo vision;Fish behaviour;Fish length;Oriented bounding box;Keypoint detection;Aquaculture |
| Auteurs | | Top |
- Zhang, Q.
- Liu, D.
- Parmiggiani, A.
|
- Norton, T., meer
- Fore, M.
- Kelasidi, E.
|
|
| Abstract |
The shift towards Precision Fish Farming (PFF) requires reliable, automated tracking of individual fish in dense, dynamic underwater environments. Many approaches targeting this topic are based on using computer vision methods and AI to detect and track the fish automatically. Most conventional approaches rely on the use of Horizontal Bounding Boxes (HBBs), however this approach has proven inadequate for accurately localising slender fish with variable orientation, particularly in scenes where fish density and occlusion is high. This study presents a new computer stereo vision framework that leverages Oriented Bounding Boxes (OBBs) for fish detection followed by keypoint detection for the localisation of the anatomical features of the detected fish. The method was set up to directly estimate 3D positions of the detected fish and keypoints and track these over time without external calibration targets. When applied to stereo video data collected from two field trials at commercial fish farms, the method estimated fish-camera distances and fish lengths that corresponded with reference data obtained through other sensors and manual sampling. These results imply that this new approach represents a step forward in non-invasive, automated monitoring and analysis of fish behaviour and growth, giving more detailed insights into farmed fish dynamics and contributing to further progress in the realisation of PFF. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
