Visual Scene Understanding in Underwater Environments

The utilization of Autonomous Underwater Vehicles (AUVs) represents a significant advancement in the field of seabed monitoring. However, image processing of data acquired from AUVs presents a unique challenge due to the inherent properties of the underwater environment, such as light attenuation and water turbidity. This work investigates techniques to enhance underwater scene understanding from monocular images. The proposed system leverages existing deep learning methods in conjunction with simple image processing algorithms, eliminating the need for additional supervised training. The system studies the combinatio of a pre-trained deep learning model, for depth estimation from monocular images, with the proposed algorithm to distinguish water regions from the rest of the scene elements. The presented study includes comprehensive comparison of various system alternatives and configuration options. The experimental validation shows how the presented system obtains richer segmentation results compared to baseline algorithms. Notably, the proposed system facilitates the accurate segmentation of water regions and enables the detection of other objects of interest, including suspended elements in the water, which can potentially correspond to fish or other mobile obstacles.