In:
Journal of Marine Science and Engineering, MDPI AG, Vol. 11, No. 6 ( 2023-05-23), p. 1103-
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.
Type of Medium:
Online Resource
ISSN:
2077-1312
DOI:
10.3390/jmse11061103
Language:
English
Publisher:
MDPI AG
Publication Date:
2023
detail.hit.zdb_id:
2738390-8
