Underwater image saliency detection based on refined attentional feedback mechanism

Meisheng Liu; Weiwei Yu

doi:10.1117/12.3008023

14 November 2023 Underwater image saliency detection based on refined attentional feedback mechanism

Meisheng Liu, Weiwei Yu

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 1293419 (2023) https://doi.org/10.1117/12.3008023
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Underwater image saliency detection makes the terrestrial saliency detection model less effective in the application of underwater vehicles due to factors such as turbid water and unstable light, resulting in a degradation of the model's performance. We offer a model for underwater saliency detection based on an improved attentional feedback mechanism to overcome the aforementioned issues. The features at the top and bottom levels are effectively fused by forming a cascaded feedback decoder through the cross feature module and adding channel space attention, after which the residual refinement module is added for further refinement. The training and testing process uses underwater open datasets. The experimental findings demonstrate that our method is superior to other ways in comparative analysis with four general saliency detection methods and two underwater saliency detection methods on four underwater image datasets, proving the model's viability and efficacy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Meisheng Liu and Weiwei Yu "Underwater image saliency detection based on refined attentional feedback mechanism", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 1293419 (14 November 2023); https://doi.org/10.1117/12.3008023

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KEYWORDS

Object detection

Data modeling

Feature extraction

Education and training

Submerged target modeling

Performance modeling

Deep learning

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