江苏科技大学学报(自然科学版)2024,Vol.38Issue(2) :53-57.DOI:10.20061/j.issn.1673-4807.2024.02.009

基于注意力机制和U-net模型的水下图像分割方法研究

Underwater image segmentation based on attention mechanism and the U-net model

苑永起 田雨波 孙颖鑫 潘婷
江苏科技大学学报(自然科学版)2024,Vol.38Issue(2) :53-57.DOI:10.20061/j.issn.1673-4807.2024.02.009

基于注意力机制和U-net模型的水下图像分割方法研究

Underwater image segmentation based on attention mechanism and the U-net model

苑永起 1田雨波 2孙颖鑫 3潘婷1
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作者信息

  • 1. 江苏科技大学海洋学院,镇江 212100
  • 2. 广州航海学院信息与通信工程学院,广州 510725
  • 3. 江苏科技大学经济管理学院,镇江 212100
  • 折叠

摘要

为提升水下图像分割效果,提出一种利用三分支注意力模块改进U-net结构的水下图像分割方法,即利用注意力机制来实现跨通道交互信息,在实现多维交互的同时不降低维度.通过在VOC2007和SUIM数据集上的实验表明,文中方法在VOC2007数据集上mIOU值为72.05,mPA值为81.3,优于传统U-net网络mIOU值58.74和mPA值71.13;在SUIM水下数据集上mIOU值为70.374,mPA值为82.838,优于传统U-net网络mIOU值68.89和mPA值82.51,能够更好的进行水下图像分割.

Abstract

To improve the effect of underwater image segmentation,this paper proposes an underwater image seg-mentation model that uses a three-branch attention module to enhance the U-net model.Specifically,the atten-tion mechanism achieves cross-channel interactive information,which realized multi-dimensional interaction with-out reducing the dimensionality.The experiments on the VOC2007 and SUIM datasets show that the mIOU value of the proposed method is 72.05 and the mPA value 81.3 on the VOC2007 dataset,which are better than the traditional U-net network with mIOU value 58.74 and mPA value 71.13.The mIOU value is 70.374 and the mPA value 82.838 of the proposed method on SUIM dataset,which is better than the traditional U-net network's mIOU value 68.89 and mPA value 82.51.The proposed method can better perform underwater image segmen-tation.

关键词

注意力机制/图像分割/水下图像/深度学习

Key words

attention mechanism/image segmentation/underwater image/deep learning

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出版年

2024
江苏科技大学学报(自然科学版)
江苏科技大学

江苏科技大学学报(自然科学版)

影响因子:0.373
ISSN:1673-4807
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