Pavement subsurface distress endangers driving safety and road serviceability. Ground penetrating radar (GPR) can non-destructively provides high-resolution profiles of road. However, the automatic interpretation of radar signals remains challenging. This study proposed an automatic pavement subsurface distress detection method using traditional signal processing and deep learning. Firstly, a piecewise linear function for radar signal automatic gain was proposed. Wavelet transform was applied to identify road layers for function segmentation. Each signal's power spectral density was calculated to determine the gain function coefficient. A specific pseudo-color mapping method was designed to convert reflected signal for deep learning model training. A radar reflection simulation was built to pre-train the model to enhance model performance. More than 270 km of field test data were collected for model training and validation. The proposed model has shown a 72.39% accuracy in shallow cracks and 68.74% in subsurface voids.

路面地下遇险危及驾驶安全和道路可使用性。探地雷达 (GPR) 可以无损地提供高分辨率的道路剖面。然而，雷达信号的自动解释仍然具有挑战性。本研究提出了一种使用传统信号处理和深度学习的自动路面地下故障检测方法。首先，提出了雷达信号自动增益的分段线性函数。应用小波变换来识别道路层以进行功能分割。计算每个信号的功率谱密度以确定增益函数系数。设计了一种特定的伪彩色映射方法，将反射信号转换为深度学习模型训练。建立了雷达反射模拟来预训练模型以提高模型性能。收集了超过 270 公里的现场测试数据用于模型训练和验证。所提出的模型在浅裂缝中显示出 72.39% 的准确度，在地下空隙中显示出 68.74% 的准确度。