Application of image processing and convolutional neural networks for flood image classification and semantic segmentation
Application of image processing and convolutional neural networks for flood image classification and semantic segmentation
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DOI：
10.1016/j.envsoft.2021.105285
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发表时间：
2021-12
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影响因子：
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通讯作者：
Jaku Rabinder Rakshit Pally;jpally
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作者：
Jaku Rabinder Rakshit Pally;jpally
深度学习算法是收集和分析灾难性准备和无数可操作洪水数据的非常有价值的工具。卷积神经网络（CNN）是计算机视觉中广泛使用的深度学习算法的一种形式，可用于研究洪水图像并为图像中的各种对象分配可学习的权重。在这里，我们利用并讨论了如何使用连接的视觉系统来嵌入摄像头、图像处理、CNN和数据连接功能，以进行洪水标签检测。我们构建了一个包含>9000张图像的训练数据库服务（图像注释服务），其中包括来自社交媒体平台、交通部（DOT）511交通摄像头、美国地质调查局（USGS）实时河流摄像头以及从搜索引擎下载的图像的流媒体相关图像的图像地理位置信息。然后，我们开发了一个名为“FloodImageClassifier”的新Python包，用于对收集到的洪水图像中的对象进行分类和检测。“FloodImageClassifier”包括各种CNN架构，如YOLOv 3（只看一次版本3），Fast R-CNN（基于区域的CNN），Mask R-CNN，SSD MobileNet（单次多盒检测器MobileNet）和EfficientDet（高效对象检测），以同时执行对象检测和分割。Canny边缘检测和纵横比的概念也包括在洪水水位估计和分类包。该管道设计巧妙，可以训练大量图像，计算洪水水位和淹没区域，用于识别洪水深度、严重程度和风险。“FloodImageClassifier”可以嵌入USGS实时河流摄像头和511交通摄像头，以监测河流和道路洪水状况，并实时向应急响应当局提供早期情报。
Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of >9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, Department of Transportation (DOT) 511 traffic cameras, the US Geological Survey (USGS) live river cameras, and images downloaded from search engines. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various CNNs architectures such as YOLOv3 (You look only once version 3), Fast R–CNN (Region-based CNN), Mask R–CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (Efficient Object Detection) to perform both object detection and segmentation simultaneously. Canny Edge Detection and aspect ratio concepts are also included in the package for flood water level estimation and classification. The pipeline is smartly designed to train a large number of images and calculate flood water levels and inundation areas which can be used to identify flood depth, severity, and risk. “FloodImageClassifier” can be embedded with the USGS live river cameras and 511 traffic cameras to monitor river and road flooding conditions and provide early intelligence to emergency response authorities in real-time.