CE$^{3}$USOD: Channel-Enhanced, Efficient, and Effective Network for Underwater Salient Object Detection

Underwater salient object detection (USOD) aims to identify the most crucial elements in underwater environments, holding significant potential for underwater exploration. Existing methods often overlook light degradation or involve larger network sizes, which are unsuitable for underwater mobile platforms and pose challenges to implement in practice. Given the importance of low-complexity algorithms in underwater applications to optimize system efficiency, this article introduces CE<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math> </inline-formula>USOD—an efficient network tailored to deliver an effective solution for salient object detection in underwater scenarios. On the one hand, we reconsider long-range dependencies and feature computation from a neighborhood perspective, leading to the development of the long-range context-aware module. Specifically, we approximate local and global context awareness by incorporating the maximum and average values of neighboring pixels within varying window sizes, which allows our method to achieve high performance while maintaining low computational cost. On the other hand, light scattering and absorption during underwater imaging frequently result in channel intensity imbalances in captured underwater images. To address this, we propose the color-guided pyramid aggregation module, which utilizes the weaker color channels enhanced by underwater image enhancement techniques as guiders for multiscale feature fusion, finally facilitating the model to obtain underwater domain information. Extensive experiments on four public benchmarks demonstrate that our innovative network achieves state-of-the-art results while maintaining a low model size (Params of 0.546M) and computational complexity (FLOPs of 0.416G). Therefore, CE<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math> </inline-formula>USOD proves to be effective and efficient, establishing its practicality, particularly for underwater applications.

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Publication Details

Journal
IEEE Journal of Oceanic Engineering
Published
2025-02-26
DOI
https://doi.org/10.1109/joe.2024.3523356
Citations
2
Primary Topic
Underwater Acoustics Research
Type
article
Field-Weighted Citation Impact
3.59

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article

CE$^{3}$USOD: Channel-Enhanced, Efficient, and Effective Network for Underwater Salient Object Detection

Zhenqi Fu, Qifei Wu, Jiaxin Xie, Xinghao Ding et al.
2 citations
IEEE Journal of Oceanic Engineering
Underwater Acoustics Research
3.59
article

CE$^{3}$USOD: Channel-Enhanced, Efficient, and Effective Network for Underwater Salient Object Detection

Zhenqi Fu, Qifei Wu, Jiaxin Xie, Xinghao Ding, Xiaotong Tu, Yue Huang
article en
2 citations

Abstract

Underwater salient object detection (USOD) aims to identify the most crucial elements in underwater environments, holding significant potential for underwater exploration. Existing methods often overlook light degradation or involve larger network sizes, which are unsuitable for underwater mobile platforms and pose challenges to implement in practice. Given the importance of low-complexity algorithms in underwater applications to optimize system efficiency, this article introduces CE<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math> </inline-formula>USOD—an efficient network tailored to deliver an effective solution for salient object detection in underwater scenarios. On the one hand, we reconsider long-range dependencies and feature computation from a neighborhood perspective, leading to the development of the long-range context-aware module. Specifically, we approximate local and global context awareness by incorporating the maximum and average values of neighboring pixels within varying window sizes, which allows our method to achieve high performance while maintaining low computational cost. On the other hand, light scattering and absorption during underwater imaging frequently result in channel intensity imbalances in captured underwater images. To address this, we propose the color-guided pyramid aggregation module, which utilizes the weaker color channels enhanced by underwater image enhancement techniques as guiders for multiscale feature fusion, finally facilitating the model to obtain underwater domain information. Extensive experiments on four public benchmarks demonstrate that our innovative network achieves state-of-the-art results while maintaining a low model size (Params of 0.546M) and computational complexity (FLOPs of 0.416G). Therefore, CE<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math> </inline-formula>USOD proves to be effective and efficient, establishing its practicality, particularly for underwater applications.

IEEE Journal of Oceanic EngineeringVol. 50(2)
Xiamen University (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Life below water
Openalex Percentile: Top 10%
Underwater Acoustics Research
3.59
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