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A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network

机译：基于面罩区域的卷积神经网络测量城市水涝深度的新方法

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Quickly obtaining accurate waterlogging depth data is vital in urban flood events, especially for emergency response and risk mitigation. In this study, a novel approach to measure urban waterlogging depth was developed using images from social networks and traffic surveillance video systems. The Mask region-based convolutional neural network (Mask R-CNN) model was used to detect tires in waterlogging, which were considered to be reference objects. Then, waterlogging depth was calculated using the height differences method and Pythagorean theorem. The results show that tires detected from images can been used as an effective reference object to calculate waterlogging depth. The Pythagorean theorem method performs better on images from social networks, and the height differences method performs well both on the images from social networks and on traffic surveillance video systems. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.
机译：快速获得准确的水涝深度数据在城市洪水事件中至关重要，特别是对于应急响应和风险缓解。在这项研究中，使用来自社交网络和交通监控视频系统的图像开发了一种衡量城市涝渍深度的新方法。基于掩模区域的卷积神经网络（掩模R-CNN）模型用于检测涝渍的轮胎，被认为是参考物体。然后，使用高度差异方法和毕达哥兰定理来计算涝渍深度。结果表明，从图像中检测到的轮胎可以用作有效的参考对象来计算涝渍深度。 Pythagorean定理方法在社交网络中的图像上执行更好，并且高度差异方法在社交网络和交通监控视频系统上的图像上执行良好。总体而言，本研究中提出的低成本方法可用于获得及时的涝渍警告信息，并增强使用现有社交网络和交通监控视频系统进行机会涝渍感的可能性。

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