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首页> 外文会议>2019 10th International Conference on Dependable Systems, Services and Technologies >Using Deep Learning for IoT-enabled Camera: A Use Case of Flood Monitoring
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Using Deep Learning for IoT-enabled Camera: A Use Case of Flood Monitoring

机译：将深度学习用于支持IoT的摄像头：洪水监控的用例

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摘要

In recent years, deep learning has been increasingly used for several applications such as object analysis, feature extraction and image classification. This paper explores the use of deep learning in a flood monitoring application in the context of an EC-funded project, Smart Cities and Open Data REuse (SCORE). IoT sensors for detecting blocked gullies and drainages are notoriously hard to build, hence we propose a novel technique to utilise deep learning for building an IoT-enabled smart camera to address this need. In our work, we apply deep leaning to classify drain blockage images to develop an effective image classification model for different severity of blockages. Using this model, an image can be analysed and classified in number of classes depending upon the context of the image. In building such model, we explored the use of filtering in terms of segmentation as one of the approaches to increase the accuracy of classification by concentrating only into the area of interest within the image. Segmentation is applied in data pre-processing stage in our application before the training. We used crowdsourced publicly available images to train and test our model. Our model with segmentation showed an improvement in the classification accuracy.
机译：近年来，深度学习已越来越多地用于多种应用程序，例如对象分析，特征提取和图像分类。本文探讨了在EC资助的项目“智能城市和开放数据重用（SCORE）”的背景下，深度学习在洪水监控应用中的使用。众所周知，很难检测到堵塞的沟渠和排水的物联网传感器，因此，我们提出了一种新颖的技术，可以利用深度学习来构建具有物联网功能的智能相机来满足这一需求。在我们的工作中，我们运用深度学习对排水堵塞图像进行分类，以针对不同严重程度的堵塞情况开发有效的图像分类模型。使用此模型，可以根据图像的上下文分析图像并将其分类为若干类。在建立这样的模型时，我们探索了使用分割法进行过滤的方法，该方法是通过仅集中于图像内感兴趣的区域来提高分类准确性的方法之一。在训练之前，在我们的应用程序中将细分应用于数据预处理阶段。我们使用众包的公共可用图像来训练和测试我们的模型。我们的带分割模型显示出分类精度的提高。

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