# Attention Neural Network for Water Image Classification under IoT Environment
###### Abstract  
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. t 202020109  
delay. Core challenges of such monitoring system are thus to effectively analyze data captured by IoT technologies for detailed pollution information. Among multiple water-relevant data types, we focus on one of the most common categories, i.e., water imagery. Furthermore, we perform image content understanding to achieve goal of water pollution monitoring. More precisely, we aim to construct a novel water pollution monitoring system, which could perform two classification tasks under the environment of IoT. First, such system can classify input water images into basic type, i.e., clean or polluted, thus knowing where pollution is happening. On the basis of general type of water images, the system should decide subcategories of input water images, which could provide sufficient information for users to take further actions. In other words, such system can not only classify clean water images into four subcategories, i.e., fountain, lake, ocean, and river, but also know what type of water pollution is happening, such as fungus, dead animals, industrial pollution, oil, and rubbish. Based on the above goal and analysis of water image based monitoring system, the workflow for water pollution monitor can be viewed in Figure 1, in which we utilize analysis models to classify IoT captured water images into two categories and 10 subcategories. With the help of such water pollution monitoring system, users can monitor water pollution breaks in real-time and take instant actions to deal with pollution. In other words, we aim to accurately classify categories of water images to implement water pollution monitoring system. Since water images captured from environment of IoT generally suffer from artifacts, i.e., illumination variation, low contrast and complex background, it is a great challenge to accurately and efficiently perform classification tasks on water images [6]. The task of water image classification is difficult due to inherent ambiguity and low quality of IoT captured images. To solve this major problem of building water monitoring system, many works have been published to theoretically understand inherent meanings of water images. For example, [PERSON] et al. [7] fused color feature and optical flow to perform water identification, which achieves high performance in accuracy. Recently, [PERSON] et al. [8] built probability-based classifiers with spatiotemporal invariant descriptors to perform tasks of water detection.