Automatic identification of GPR targets on roads based on CNN and Grad-CAM

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.