GPA based domain adaptive feature refinement method for underwater target detection

LIU Qidong; SHEN Xin; LIU Hailu; CONG Lu; FU Xianping

doi:10.11993/j.issn.2096-3920.2023-0149

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LIU Qidong, SHEN Xin, LIU Hailu, CONG Lu, FU Xianping. GPA based domain adaptive feature refinement method for underwater target detection[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0149

Citation:

LIU Qidong, SHEN Xin, LIU Hailu, CONG Lu, FU Xianping. GPA based domain adaptive feature refinement method for underwater target detection[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0149

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GPA based domain adaptive feature refinement method for underwater target detection

doi: 10.11993/j.issn.2096-3920.2023-0149

2.
Dalian Maritime University, Da Lian 116026,China
3.
The Peng Cheng Laboratory, Shenzhen 518000, China

Received Date: 2023-11-22
Accepted Date: 2024-01-05
Rev Recd Date: 2024-01-01

Available Online: 2024-02-01

Abstract

Abstract

Underwater environments are often more susceptible to domain shift and reduced detection accuracy during underwater object detection due to the influence of lighting, sediment, and other factors. In response to this phenomenon, this article proposes a domain adaptive underwater target detection method based on graph induced alignment. Graph induced prototype alignment (GPA) obtains instance level features in the image through graph based information propagation between region proposals, and then derives prototype representations for each category for category level domain alignment. The above operations can effectively aggregate different modal information of underwater targets, thereby achieving alignment between the source and target domains and reducing the impact of domain offset. In addition, in order to focus the neural network on instance level features under different water distribution, a Convolutional Block Attention module (CBAM) was also added to it. The experimental results have shown that GPA can effectively align instance features in the source and target domains in underwater environments, while CBAM can make the network pay more attention to instance features in images and improve detection accuracy.
- target detection,
- figure induced prototype alignment,
- domain adaptation,
- attention mechanism

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References(36)

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