Real-time Seafloor Segmentation and Mapping
Journal:
arXiv
Published Date:
Apr 14, 2025
Abstract
Posidonia oceanica meadows are a species of seagrass highly dependent on
rocks for their survival and conservation. In recent years, there has been a
concerning global decline in this species, emphasizing the critical need for
efficient monitoring and assessment tools. While deep learning-based semantic
segmentation and visual automated monitoring systems have shown promise in a
variety of applications, their performance in underwater environments remains
challenging due to complex water conditions and limited datasets. This paper
introduces a framework that combines machine learning and computer vision
techniques to enable an autonomous underwater vehicle (AUV) to inspect the
boundaries of Posidonia oceanica meadows autonomously. The framework
incorporates an image segmentation module using an existing Mask R-CNN model
and a strategy for Posidonia oceanica meadow boundary tracking. Furthermore, a
new class dedicated to rocks is introduced to enhance the existing model,
aiming to contribute to a comprehensive monitoring approach and provide a
deeper understanding of the intricate interactions between the meadow and its
surrounding environment. The image segmentation model is validated using real
underwater images, while the overall inspection framework is evaluated in a
realistic simulation environment, replicating actual monitoring scenarios with
real underwater images. The results demonstrate that the proposed framework
enables the AUV to autonomously accomplish the main tasks of underwater
inspection and segmentation of rocks. Consequently, this work holds significant
potential for the conservation and protection of marine environments, providing
valuable insights into the status of Posidonia oceanica meadows and supporting
targeted preservation efforts
