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.
Artificial Intelligence for Automated Marine Growth Segmentation
Lect. Notes in Networks, Syst.
Iberian Robotics conference ; 2023 ; Coimbra, Portugal November 22, 2023 - November 24, 2023
2024-04-27
12 pages
Article/Chapter (Book)
Electronic Resource
English
Artificial Intelligence for Automated Marine Growth Segmentation
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