基于深度学习的道路积水智能监测方法
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TP399

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水资源与水电工程科学国家重点实验室(武汉大学)开放研究基金(2016HLG01);陕西省国际科技合作交流计划(2017KW-014)


Intelligent monitoring method for road inundation based on deep learning
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    摘要:

    针对目前城市区域内涝频发且传统内涝积水监测方法具有危险性大、成本较高及时效性较低等问题,提出了一种利用深度学习技术的城市道路积水快速监测方法,该方法基于卷积神经网络,可对输入积水图像数据集进行积水特征提取。选取西安理工大学校内积水情况进行验证,结果表明该方法对数据集的训练和验证的平均准确率分别为96.1%和90.1%,能较准确实现图像积水面积自动提取,从而实现城市内涝监控图像中的积水区域自动识别和积水面积自动获取。

    Abstract:

    To solve the problems including frequent urban inundation and unsafe, costly and inefficient traditional monitoring urban inundation method, a method of detecting urban road flood rapidly based on deep learning techniques is proposed. This method, based on convolutional neural network, can extract the features of puddles from the input accumulated water image data set. The water accumulation in Xian University of Technology is selected for verification. It is shown that the average recognition accuracy of the method for training and verification of the data set is 96. 1% and 90. 1% respectively, and automatic extraction of the puddle area in the image is realized accurately. So that the automatic identification of the inundation area and the automatic acquisition of the water area in the urban inundation monitoring image are realized.

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白岗岗,侯精明,韩浩,等.基于深度学习的道路积水智能监测方法[J].水资源保护,2021,37(5):75-80.(BAI Ganggang, HOU Jingming, HAN Hao, et al. Intelligent monitoring method for road inundation based on deep learning[J]. Water Resources Protection,2021,37(5):75-80.(in Chinese))

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  • 收稿日期:2020-05-14
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  • 在线发布日期: 2021-11-12
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