Abstract
Marine growth impacts the stability and integrity of offshore structures, while simultaneously preventing inspection procedures. In consequence, companies need to employ specialists that manually assess each impacted part of the structure. Due to harsh sub-sea environments, acquiring large quantities of quality underwater data becomes difficult. To mitigate these challenges a new data augmentation algorithm is proposed that generates new images by performing localized crops on regions of interest from the original data, expanding the total size of the dataset approximately 6 times. This research also proposes a learning-based algorithm capable of automatically delineating marine growth in underwater images, achieving up to 0.389 IoU and 0.508 Dice Loss. Advances in this area contribute for reducing the manual labour necessary to schedule maintenance operations in man-made submerged structures, while increasing the reliability and automation of the process.
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Acknowledgments
This work is partly funded by the Portuguese Government through the Fundação para a Ciência e a Tecnologia (FCT) within P.hD. grant 2020.06949.BD (to PNL), and by the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement No. 871571 (ATLANTIS).
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Carvalho, J., Leite, P.N., Mina, J., Pinho, L., Gonçalves, E.P., Pinto, A.M. (2024). Artificial Intelligence for Automated Marine Growth Segmentation. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_13
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