This study combines ground penetrating radar(GPR)and convolutional neural networks for the intelligent detection of underground road targets.The target location was realized using a gradient-class activation map(Grad-...This study combines ground penetrating radar(GPR)and convolutional neural networks for the intelligent detection of underground road targets.The target location was realized using a gradient-class activation map(Grad-CAM).First,GPR technology was used to detect roads and obtain radar images.This study constructs a radar image dataset containing 3000 underground road radar targets,such as underground pipelines and holes.Based on the dataset,a ResNet50 network was used to classify and train different underground targets.During training,the accuracy of the training set gradually increases and finally fluctuates approximately 85%.The loss function gradually decreases and falls between 0.2 and 0.3.Finally,targets were located using Grad-CAM.The positioning results of single and multiple targets are consistent with the actual position,indicating that the method can eff ectively realize the intelligent detection of underground targets in GPR.展开更多
以广州热带海洋气象研究所2008年2月18日00:00数值模式预报产品和以闪电定位资料统计的2007年广州总闪次数分布为实例,详细介绍了如何利用GrADS系统自动的转换工具lats4d,以不同的具体参数形式进行NetCDF格式转换,最后输出通用NetCDF格...以广州热带海洋气象研究所2008年2月18日00:00数值模式预报产品和以闪电定位资料统计的2007年广州总闪次数分布为实例,详细介绍了如何利用GrADS系统自动的转换工具lats4d,以不同的具体参数形式进行NetCDF格式转换,最后输出通用NetCDF格式,并直接在Integrated Data Viewer上进行显示。展开更多
基金supported in part by the National Natural Science Fund of China under Grant 52074306in part by the National Key Research and Development Program of China under Grant 2019YFC1805504in part by the Fundamental Research Funds for the Central Universities under Grant 2023JCCXHH02。
文摘This study combines ground penetrating radar(GPR)and convolutional neural networks for the intelligent detection of underground road targets.The target location was realized using a gradient-class activation map(Grad-CAM).First,GPR technology was used to detect roads and obtain radar images.This study constructs a radar image dataset containing 3000 underground road radar targets,such as underground pipelines and holes.Based on the dataset,a ResNet50 network was used to classify and train different underground targets.During training,the accuracy of the training set gradually increases and finally fluctuates approximately 85%.The loss function gradually decreases and falls between 0.2 and 0.3.Finally,targets were located using Grad-CAM.The positioning results of single and multiple targets are consistent with the actual position,indicating that the method can eff ectively realize the intelligent detection of underground targets in GPR.
文摘以广州热带海洋气象研究所2008年2月18日00:00数值模式预报产品和以闪电定位资料统计的2007年广州总闪次数分布为实例,详细介绍了如何利用GrADS系统自动的转换工具lats4d,以不同的具体参数形式进行NetCDF格式转换,最后输出通用NetCDF格式,并直接在Integrated Data Viewer上进行显示。
