Attention neural network for water image classification under IoT environment

Yirui Wu, Xuyun Zhang, Yao Xiao, Jun Feng

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
24 Downloads (Pure)


With significant development of sensors and Internet of things (IoT), researchers nowadays can easily know what happens in water ecosystem by acquiring water images. Essentially, growing data category and size greatly contribute to solving water pollution problems. In this paper, we focus on classifying water images to sub-categories of clean and polluted water, thus promoting instant feedback of a water pollution monitoring system that utilizes IoT technology to capture water image. Due to low inter-class and high intra-class differences of captured water images, water image classification is challenging. Inspired by the ability to extract highly distinguish features of Convolutional Neural Network (CNN), we aim to construct an attention neural network for IoT captured water images classification that appropriately encodes channel-wise and multi-layer properties to accomplish feature representation enhancement. During construction, we firstly propose channel-wise attention gate structure and then utilize it to construct a hierarchical attention neural network in local and global sense. We carried out comparative experiments on an image dataset about water surface with several studies, which showed the effectiveness of the proposed attention neural network for water image classification. We applied the proposed neural network as a key part of a water image based pollution monitoring system, which helps users to monitor water pollution breaks in real-time and take instant actions to deal with pollution.

Original languageEnglish
Article number909
Pages (from-to)1-16
Number of pages16
JournalApplied Sciences
Issue number3
Publication statusPublished - 1 Feb 2020

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Theoretical understanding of AI in the IoT
  • Feature representation enhancement
  • Intelligent network
  • Attention neural network
  • Multi-layer and channel-wise property


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