Research on 3D ground penetrating radar deep underground cavity identification algorithm in urban roads using multi-dimensional time-frequency features

The 3D ground penetrating radar (GPR) is the main method for detecting underground cavities in urban roads. Due to the weak reflected signal energy of deep road cavities with depths exceeding 2.5 m, there is a significant shortage of available training samples. Existing identification algorithms primarily focus on the detection of shallow road cavities. As a result, the accuracy of deep cavity identification using 3D GPR is low, and there is a lack of effective intelligent algorithms for deep cavity identification. To address these challenges, this study integrates the smooth texture features and the abundant amplitude and phase spectrum features inherent in deep cavity GPR signals. Utilizing the time-frequency features of radar signals, this study has proposed an intelligent identification algorithm for the deep road cavity based on a Multi-Channel and Dimensional Time-Frequency Convolution Neural Network (MCD-TF CNN). Firstly, using MCD-FT CNN as the cavity value discriminator, inverse reinforcement learning is performed on the cavity region to obtain the value evaluation method of the 3D GPR cavity region. Subsequently, the discriminator is applied to deep radar data for cavity detection and interacts with the value discriminator through 3D target region range adjustment actions. The interaction through region adjustments aims to maximize the value of cavity areas within the discriminator relative to the target detection area. It can ensure the inclusion of cavity regions in the detection results, thereby achieving the goal of intelligent deep cavity recognition. This algorithm employs the thought of global relative optimality from reinforcement learning, addressing the limitations of existing algorithms that rely on extracting absolute features and thereby enhancing the accuracy of deep cavity identification.