Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method

Detection of subsurface road targets is a crucial task in road engineering. This study focuses on detecting three types of subsurface targets: looseness, pipeline, and voids. Ground-penetrating radar (GPR) was employed to acquire real-world data. gprMax was utilized to generate additional data to address the scarcity of the original dataset. Recognizing the substantial disparity between directly simulated gprMax data and actual GPR images, this paper introduces a novel method for synthesizing gprMax-generated data with real measurements, thereby achieving effective GPR image augmentation. Furthermore, a generative adversarial network (GAN) was employed to rapidly produce large volumes of GPR images. Deep learning models were implemented to detect subsurface road targets using datasets of varying scales. Experimental results indicate that data augmentation utilizing gprMax and GAN can substantially improve the detection accuracy for subsurface road targets, achieving a rate of 0.767. This represents a 21.2% enhancement, compared to the results obtained from training on the original dataset. The findings of this research hold practical significance for supporting road maintenance operations.