Pré-Publication, Document De Travail Année : 2025

Underwater Netting Detection and Distance Estimation using Optic Flow

Résumé

Obstacle detection and avoidance remains a major challenge for automatic underwater navigation. While there exists abundant literature on this topic, certain types of poorly defined obstacles are difficult to detect, such as underwater netting. Optic Flow is a bio-inspired optical cue widely used in land-based autonomous navigation and by autonomous aircraft. Requiring only small sensors or a monocular camera, it is a minimalist but reliable source of information. However, because of the low visibility, optical distortions and overall poor quality of optical measurements underwater, examples of underwater applications of this technique remains scarce.

This study proposes an underwater netting detection algorithm called uNOWA (underwater Netting Optical floW detection Algorithm), adapted to the specifics of underwater vision. This method also estimate the distance between the camera and the net, by combining the translational optic flow with the velocity. A number of experiments have been carried out under real-life underwater conditions using different nets. The results show an effective net detection and give an estimation of the distance to the detected netting.

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Cite 10.57745/5VJQHM Jeu de données DAINI, X. (2025). Camera Underwater images in front of underwater nettings at Lake Guerlédan [Dataset]. Recherche Data Gouv. https://doi.org/10.57745/5VJQHM

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hal-05120953 , version 1 (19-06-2025)

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  • HAL Id : hal-05120953 , version 1

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Xavier Daïni, Loïck Degorre, Christophe Viel, Romain Raffin, Thibaut Raharijaona, et al.. Underwater Netting Detection and Distance Estimation using Optic Flow. 2025. ⟨hal-05120953⟩
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