Segmentation of underwater fish in complex aquaculture environments using enhanced Soft Attention Mechanism

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-08-05 DOI:10.1016/j.envsoft.2024.106170
Dashe Li , Yufang Yang , Siwei Zhao , Jinqiang Ding
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Abstract

Underwater fish segmentation technology serves as a crucial foundation for extracting aquatic biological information. However, due to intricate and fluctuating underwater environments, existing segmentation models fail to precisely focus on key image regions. Based on this, the paper developed an underwater fish segmentation model, Receptive Field Expansion Model(RFEM), by enhancing soft attention performance (More attention is directed to fish regions when processing fish pixels). This paper tests ten different attention mechanisms and selects the attention mechanism with better performance indicators to improve it and form an RFEM model. This paper uses two underwater fish data sets to verify the proposed model. The experimental results show the segmentation mean intersection-over-union ratio (MIoU) of RFEM based on dilation convolution reached 88.37%, and the mCPA reached 93.83%, Accuracy reached 96.08%, and F1-score reached 93.74%. It can provide solid technical support for intelligent monitoring such as body length measurement, weight estimation of underwater fish.

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利用增强型软注意力机制分割复杂水产养殖环境中的水下鱼类
水下鱼类分割技术是提取水生生物信息的重要基础。然而,由于水下环境复杂多变,现有的分割模型无法精确聚焦关键图像区域。基于此,本文开发了一种水下鱼类分割模型--感知场扩展模型(RFEM),该模型通过增强软注意力性能(在处理鱼类像素时,更多注意力被引导到鱼类区域)来实现。本文测试了十种不同的注意机制,并选择性能指标较好的注意机制进行改进,形成 RFEM 模型。本文使用两个水下鱼类数据集来验证所提出的模型。实验结果表明,基于扩张卷积的RFEM的分割平均交集重合率(MIoU)达到88.37%,mCPA达到93.83%,准确率达到96.08%,F1-score达到93.74%。它可以为水下鱼类的体长测量、体重估算等智能监测提供坚实的技术支持。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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