摘要
中国内涝灾害频发,尤其是发生在道路上的内涝灾害,严重影响了人们的正常出行,甚至是威胁生命,目前的道路积水监测技术效率低,迫切需要一种高效监测道路积水的方法。道路积水的精准监测有助于政府下达政策,人员做好预防,提出了一种基于改进YOLOv8的道路积水实时监测方法,在基于YOLOv8的算法下,在颈部结构网络加入了CBAM(Convolutional Block Attention Module)注意力机制,增强积水区域的重要特征并抑制一般特征,提高识别道路积水的准确率,并利用透视变换和像素来计算积水面积。选取河北工程大学新校区校内道路积水进行研究,结果表明,该方法的精确率达到93.83%,能精确实时识别出道路积水路面并输出积水面积,满足监测需求。
Flooding disasters occur frequently in China,especially on roads,which seriously affect people's normal travel and even threaten their lives.The current technologies for monitoring road waterlogging are inefficient,and there is an urgent need for an efficient method to monitor road waterlogging.Accurate monitoring of road waterlogging is helpful for the government to issue policies and personnel to take preventive measures.Therefore,this article proposed a real-time monitoring method for road waterlogging based on improved YOLOv8.Through the YOLOv8 algorithm,a convolutional block attention module(CBAM)attention mechanism was added to the neck structure network to enhance the important features of waterlogging areas,suppress general features,and improve the accuracy of identifying road waterlogging.In addition,perspective transformation and pixels were used to calculate the waterlogging area.The article studied the road waterlogging in the new campus of Hebei University of Engineering.The results show that the accuracy of this method reaches 93.83%,which can accurately identify the road waterlogging surface and output the waterlogging area in real time,meeting the monitoring needs.
作者
张峥
左向阳
龙岩
黄浩成
何立新
雷晓辉
王萌茜
ZHANG Zheng;ZUO Xiangyang;LONG Yan;HUANG Haocheng;HE Lixin;LEI Xiaohui;WANGMengqian(School of Water Resources and Hydropower,Hebei University of Engineering,Handan 056038,China;Hebei Smart Water Conservancy Key Laboratory,Handan 056038,China;School of Management,Hefei University of Technology,Hefei 230000,China;MWR General Institute of Water Resources and Hydropower Planning and Design,Beijing 100000,China)
出处
《人民珠江》
2024年第10期44-50,共7页
Pearl River
基金
河北省高等学校科学技术研究项目资助(BJK2022038)
河北省自然科学青年基金(E2021402039)。
关键词
内涝灾害
深度学习
注意力机制
道路积水
waterlogging disaster
deep learning
attention mechanism
road waterlogging