基于无人机影像深度学习的滑坡灾害智能识别

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中文摘要：为精确识别和预警露天矿滑坡灾害,提出一种基于面向对象的标注数据集和Res-U-Net模型相结合的露天矿滑坡智能识别方法.首先,以无人机航测获取研究区矿山滑坡影像数据;其次,采用多尺度-光谱差异分割方法和阈值分离原理,对露天矿滑坡数据进行分割和分类,完成基于面向对象方法的滑坡数据集构建;然后,以U-Net网络作为基础架构,在每个卷积层融入ResNet的残差模块,构建基于Res-U-Net的滑坡识别语义分割模型;最后,识别不同方法构建的滑坡数据集,并对比Res-U-Net模型与主流的语义分割模型全卷积神经网络(FCN)、U-net.结果表明:基于面向对象标注的滑坡数据集相比于传统人工标注数据集具有更好的滑坡识别效果,在准确率、召回率、F1分数和kappa系数上都有12％以上的提升;Res-U-Net模型的滑坡识别精度均在0.8以上,实现露天矿山滑坡灾害精准识别.

外文标题：Intelligent identification of landslide disaster based on deep learning of UAV images

外文摘要：An open-pit mine landslide identification method was proposed based on object-oriented annotation datasets and the Res-U-Net model to realize accurate identification and early warning of open-pit mile landslide disasters.Firstly,the mine landslide image data in the study area were obtained by UAV aerial survey.Secondly,the multi-scale-spectral segmentation method and threshold separation principle were applied to divide and classify the open-pit mine landslide data,and the landslide dataset was developed based on the object-oriented method.Then,the U-Net network was used as the infrastructure to propose a landslide identification semantic segmentation model based on Res-U-Net by integrating the residual module into each convolutional layer.Finally,the datasets constructed by different methods were used to identify landslides,and the Res-U-Net model was compared with the widely used semantic segmentation models,Fully Convolutional Networks(FCN),and U-net.The results indicated that the landslide data set based on object-oriented annotation had better landslide identification performance when compared to the traditional manual annotation dataset,resulting in improvements in identification accuracy,recall rate,F,score,and kappa coefficient of more than 12％.The landslide identification accuracy of the Res-U-Net model was more than 0.8,realizing the accurate landslide open-pit mine disaster identification.

外文关键词：

unmanned aerial vehicle imagedeep learninglandslide disasterintelligent identificationobject orientedRes-U-Net

作者：

江松、李研博、何旭乾、何润丰、张超、张存良

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作者单位：

西安建筑科技大学资源工程学院,陕西西安 710055

西安建筑科技大学管理学院,陕西西安 710055

中钢集团马鞍山矿山研究总院有限公司,安徽马鞍山 243000

洛阳栾川钼业集团股份有限公司,河南洛阳 471500

内蒙古汇能煤电集团有限公司,内蒙古鄂尔多斯 017000

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关键词：

无人机影像深度学习滑坡灾害智能识别面向对象 Res-U-Net

基金：

国家自然科学基金青年项目资助中国博士后科学基金面上项目资助陕西省社会科学基金资助内蒙古呼和浩特市科技局项目

项目编号：

521041462022M7229252020R0052023-高-12

出版年：

2024

DOI：

10.16265/j.cnki.issn1003-3033.2024.07.2092

中国安全科学学报

中国职业安全健康协会

中国安全科学学报

CSTPCD北大核心

影响因子：1.548

ISSN：1003-3033

年,卷(期)：2024.34(7)

参考文献量10