Zhang, W.; Chen, X.; Zhou, X.; Chen, J.; Yuan, J.; Zhao, T.; Xu, K. Deep Learning-Based Geomorphic Feature Identification in Dredge Pit Marine Environment. J. Mar. Sci. Eng.2024, 12, 1091.
Zhang, W.; Chen, X.; Zhou, X.; Chen, J.; Yuan, J.; Zhao, T.; Xu, K. Deep Learning-Based Geomorphic Feature Identification in Dredge Pit Marine Environment. J. Mar. Sci. Eng. 2024, 12, 1091.
Zhang, W.; Chen, X.; Zhou, X.; Chen, J.; Yuan, J.; Zhao, T.; Xu, K. Deep Learning-Based Geomorphic Feature Identification in Dredge Pit Marine Environment. J. Mar. Sci. Eng.2024, 12, 1091.
Zhang, W.; Chen, X.; Zhou, X.; Chen, J.; Yuan, J.; Zhao, T.; Xu, K. Deep Learning-Based Geomorphic Feature Identification in Dredge Pit Marine Environment. J. Mar. Sci. Eng. 2024, 12, 1091.
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 designed to detect mine-like objects and are rarely applied to identifying features in dynamic dredge pit environments, due to a lack of datasets. 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 Mississippi bird foot delta of the Louisiana inner shelf. This dataset contains a total of 385 SSS images. We introduce an Effective Geomorphology Classification model (EGC). By 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 will make geomorphic features identification in dredge pit environments quick and efficient which requires extensive experience and professional knowledge. The combination of SSS images and EGC model is a cost-effective and valuable toolkit for hazards monitoring in marine dredge pit environment. The SPDP SSS images dataset, especially the feature of pit wall without rotational slump, is also valuable for other machine learning modelers.
Keywords
sidescan; sonar; geomorphology; deep learning; coastal restoration; EfficientNet
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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