1. A Side-Scan Sonar Image Synthesis Method Based on a Diffusion Model.
- Author
-
Yang, Zhiwei, Zhao, Jianhu, Zhang, Hongmei, Yu, Yongcan, and Huang, Chao
- Subjects
SONAR imaging ,OBJECT recognition (Computer vision) ,SONAR ,RANDOM noise theory ,HOUGH transforms ,DATA distribution - 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]
- Published
- 2023
- Full Text
- View/download PDF