Deep Learning-Based Geomorphic Feature Identification in Dredge Pit Marine Environment

Abstract

Deep learning methods paired with sidescan sonar (SSS) are commonly used in underwater search-and-rescue operations for drowning victims, wrecks, and airplanes. However, these techniques are primarily used to detect mine-like objects and are rarely applied to identifying features in dynamic dredge pit environments. In this study, we present a Sandy Point dredge pit (SPDP) dataset, in which high-resolution SSS data were collected from the west flank of the Mississippi bird-foot delta on the Louisiana inner shelf. This dataset contains a total of 385 SSS images. We then introduce a new Effective Geomorphology Classification model (EGC). Through ablation studies, we analyze the utility of transfer learning on different model architectures and the impact of data augmentations on model performance. This EGC model makes geomorphic feature identification in dredge pit environments, which requires extensive experience and professional knowledge, a quick and efficient task. The combination of SSS images and the EGC model is a cost-effective and valuable toolkit for hazard monitoring in marine dredge pit environments. The SPDP SSS image dataset, especially the feature of pit walls without a rotational slump, is also valuable for other machine learning models.