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A Side-Scan Sonar Image Synthesis Method Based on a Diffusion Model.
- Source :
- Journal of Marine Science & Engineering; Jun2023, Vol. 11 Issue 6, p1103, 16p
- Publication Year :
- 2023
-
Abstract
- The limited number and under-representation of side-scan sonar samples hinders the training of high-performance underwater object detection models. To address this issue, in this paper, we propose a diffusion model-based method to augment side-scan sonar image samples. First, the side-scan sonar image is transformed into Gaussian distributed random noise based on its a priori discriminant. Then, the Gaussian noise is modified step by step in the inverse process to reconstruct a new sample with the same distribution as the a priori data. To improve the sample generation speed, an accelerated encoder is introduced to reduce the model sampling time. Experiments show that our method can generate a large number of representative side-scan sonar images. The generated side-scan sonar shipwreck images are used to train an underwater shipwreck object detection model, which achieves a detection accuracy of 91.5% on a real side-scan sonar dataset. This exceeds the detection accuracy of real side-scan sonar data and validates the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20771312
- Volume :
- 11
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Journal of Marine Science & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 164650692
- Full Text :
- https://doi.org/10.3390/jmse11061103