Abstract 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 effectively realize the intelligent detection of underground targets in GPR.
Fund: This work was supported in part by the National Natural Science Fund of China under Grant 52074306, in part by the National Key Research and Development Program of China under Grant 2019YFC1805504 and in part by the Fundamental Research Funds for the Central Universities under Grant 2023JCCXHH02.
About author: Dou Yi-Tao is currently a master's degree candidate. She graduated from the Nanjing University
of Posts and Telecommunications with a B.S. degree in Human Geography and Urban-Rural Planning in 2021 (graduation). Since 2021, she has been pursuing her M . S . degree
in Cartography and Geographic Information System at the China University of Mining &Technology, Beijing. Her main interests are geophysical prospecting and machine learning.
Cite this article:
. Automatic identification of GPR targets on roads based on CNN and Grad-CAM[J]. APPLIED GEOPHYSICS, 2025, 22(2): 488-498.
2025, Vol. 22 
