Adapting Mask-RCNN for Instance Segmentation of Underwater Dunes in Digital Bathymetric Models

The introduction of multibeam echosounders has marked a turning point in bathymetric data acquisition, providing precise and detailed digital bathymetric models. These instruments not only enhance our understanding of underwater terrain dynamics but also reveal the presence of complex sedimentary structures, such as submarine dunes. Dunes play an important role in the preservation of the environment but can also be obstacles to safe navigation, requiring dragging operations. Hence, it is important to detect them from bathymetric models. Although information about these dunes has numerous applications, their identification methods remain poorly automated. This paper aims to leverage deep learning to develop a segmentation method for submarine dunes. Several challenges must be overcome. Dunes are complex objects with irregular, highly variable shapes, while bathymetric data are noisy and lack detailed information. Furthermore, in the fluvio-marine context, no labeled datasets exist for training purposes. Starting from a small pre-labeled dataset, this paper proposes a systematic approach to train a Mask R-CNN network. First, data augmentation techniques are applied to expand the dataset significantly and introduce meaningful variations. By relying on transfer learning with a carefully selected pre-trained backbone, feature extraction is optimized, reducing training time while enhancing model performance. The adaptation of the Mask R-CNN model to our submarine dune segmentation task has led to a significant improvement in detection performance, with a pixel-level F1-score reaching 89%. Additionally, the mean Average Precision has exceeded 50%, demonstrating the model’s effectiveness in identifying and delineating dunes despite their varied shapes and blurred contours. These results confirm the relevance of our approach for achieving more reliable dune segmentation in a complex fluvio-marine environment.