Monitoring provides important information for the planning and execution of marine environment preservation operations. Posidonia oceanica is one of the principal bioindicators in Mediterranean coastal areas and regular monitoring activities play a crucial role in its conservation. However, an efficient observation of vast areas colonised with P. oceanica is extremely challenging and it currently requires tedious and time consuming diving activities. Autonomous Underwater Vehicles (AUVs) endowed with optical sensors could represent a viable solution in carrying out visual inspection surveys. Nevertheless, AUVs are usually programmed to perform pre-defined trajectories, which are not effective for seagrass monitoring applications, as meadows may be fragmented and their contours may be irregular. This work proposes a framework based on machine learning and computer vision that enables an AUV equipped with a down-looking camera to autonomously inspect the boundary of P. oceanica meadows to obtain an initial estimate of the meadow size. The proposed boundary inspection solution is composed of three main modules: (1) an image segmentation relying on a Mask R-CNN model to recognise P. oceanica in underwater images, (2) a boundary tracking strategy that generates guidance references to track P. oceanica contours, (3) a loop closure detector fusing visual and navigation information to identify when a meadow boundary has been completely explored. The image segmentation model and the visual part of the loop closure detection module were validated on real underwater images. The overall inspection framework was tested in a realistic simulation environment, using mosaics obtained from real images to replicate the actual monitoring scenarios. The results show that the proposed solution enables the AUV to autonomously accomplish the boundary inspection task of P. oceanica meadows, therefore representing an effective tool towards the conservation and protection of marine environments.

Autonomous boundary inspection of Posidonia oceanica meadows using an underwater robot

Ruscio F.;Costanzi R.;
2023-01-01

Abstract

Monitoring provides important information for the planning and execution of marine environment preservation operations. Posidonia oceanica is one of the principal bioindicators in Mediterranean coastal areas and regular monitoring activities play a crucial role in its conservation. However, an efficient observation of vast areas colonised with P. oceanica is extremely challenging and it currently requires tedious and time consuming diving activities. Autonomous Underwater Vehicles (AUVs) endowed with optical sensors could represent a viable solution in carrying out visual inspection surveys. Nevertheless, AUVs are usually programmed to perform pre-defined trajectories, which are not effective for seagrass monitoring applications, as meadows may be fragmented and their contours may be irregular. This work proposes a framework based on machine learning and computer vision that enables an AUV equipped with a down-looking camera to autonomously inspect the boundary of P. oceanica meadows to obtain an initial estimate of the meadow size. The proposed boundary inspection solution is composed of three main modules: (1) an image segmentation relying on a Mask R-CNN model to recognise P. oceanica in underwater images, (2) a boundary tracking strategy that generates guidance references to track P. oceanica contours, (3) a loop closure detector fusing visual and navigation information to identify when a meadow boundary has been completely explored. The image segmentation model and the visual part of the loop closure detection module were validated on real underwater images. The overall inspection framework was tested in a realistic simulation environment, using mosaics obtained from real images to replicate the actual monitoring scenarios. The results show that the proposed solution enables the AUV to autonomously accomplish the boundary inspection task of P. oceanica meadows, therefore representing an effective tool towards the conservation and protection of marine environments.
2023
Ruscio, F.; Costanzi, R.; Gracias, N.; Quintana, J.; Garcia, R.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1192867
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