Abstract
Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migration-based method to collect many training data and created 53510 categorized images. We then examined the accuracy of the deep convolutional neural network in detecting the signatures. The accuracy of the classification was 0.945 (94.5%)–0.979 (97.9%) when using several thousands of training images and was much better than the accuracy of the conventional neural network approach. Our results demonstrate the effectiveness of the deep convolutional neural network in detecting characteristic events in ground-penetrating radar images.
Highlights
Ground-penetrating radar (GPR) is an effective technology for the nondestructive investigation of the shallow subsurface based on the transmitting and receiving of an electromagnetic wave
The present paper proposes the use of the deep convolutional neural network (CNN) to increase the accuracy and versatility of target detection from GPR images
We examined the accuracy of the deep CNN in detecting a characteristic reflection pattern in GPR images
Summary
Ground-penetrating radar (GPR) is an effective technology for the nondestructive investigation of the shallow subsurface based on the transmitting and receiving of an electromagnetic wave. For the automatic detection of characteristic features in GPR images, a neural network (NN)-based methodology was developed [3, 4]. The deep convolutional neural network (CNN) has been successfully used in pattern recognition (e.g., [6, 7]) and classification (e.g., [8, 9]). The deep CNN improves the accuracy of International Journal of Geophysics the target detection of GPR images and the versatility of the approach. The present paper proposes the use of the deep CNN to increase the accuracy and versatility of target detection from GPR images. Many training images are needed for the successful training of the deep CNN and improved accuracy. We developed an algorithm based on the migration procedure for the first-step extraction of training images with target signatures
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