A Domain Variable Prior Based Multi-Style Transfer Network for Data Augmentation of Tidal Stream Turbine Rotor Image Dataset

Author:

Jiang Guohan1ORCID,Wang Tianzhen1ORCID,Yang Dingding1ORCID,You Jingyi2

Affiliation:

1. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, P. R. China

2. Shanghai Marine Equipment Research Institute, Shanghai 200031, P. R. China

Abstract

The style of the underwater images varies according to the region of the sea. However, Tidal Stream Turbine (TST) rotor images captured in the laboratory environment cannot reflect the real underwater environment in image style, resulting in poor generalization of image signal-based fault detection algorithms. Due to the fixed capture position of the camera, the TST rotor image dataset has a high semantic similarity between images, resulting in content loss in conventional image-to-image translation networks. Meanwhile, the one-to-one translation feature in other works cannot meet our requirements. In this work, a Domain Variable Prior-based Multi-style Transfer Network (DVP-MSTN) is proposed to achieve TST rotor image style augmentation. First, the backbone network is trained using a public paired dataset to acquire prior knowledge of domain variable (Knowledge Acquiring, KA). Next, a Multi-domain Transfer Unit (MDT unit) is introduced to enable the conversion of style representations in low-dimensional space. Finally, the prior knowledge is shared to train the MDT unit by fixing the parameters of the backbone network optimized from the KA process (Knowledge Sharing, KS). In addition, an algorithm based on the dark channel of the image is proposed to improve the transfer of low-contrast features. Specifically, a discriminator is used to discriminate the image dark channel to guide the MDT unit to generate low-contrast style representation conditionally. Meanwhile, color loss is employed to preserve the color feature of the image. By controlling the weights of the style code, this method enables control over the image style transfer process, thereby expanding the variety of image styles in the dataset for the purpose of data augmentation.

Funder

National Natural Science Foundation of China

Publisher

World Scientific Pub Co Pte Ltd

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