A State-Space-Model-Based Hyperbola Detection Method for Arbitrarily Long GPR B-Scan

The hyperbola detection in the ground-penetrating radar (GPR) data is of real significance for subsurface object localization. However, present detection methods in GPR cannot process B-scan data with arbitrary length. In this letter, a hyperbola detection method based on the state-space model (SSM) for arbitrarily long GPR B-scan is proposed. The proposed method consists of three parts: a time dimension encoder based on the ResNet block, an SSM module, and a time dimension decoder based on the transposed convolution. First, the time dimension encoder extracts high-dimensional time dimension signal features from each scan channel of B-scan data. Then, the SSM module extracts bidirectional correlation features along the survey line from the feature maps obtained in the first part, thereby obtaining feature maps containing hyperbolic target features. Finally, the time dimension decoder decodes the features obtained in the second part and outputs the probability map representing the target hyperbolic vertex region. Based on the probability map, the hyperbola detection and localization results can be obtained. The effectiveness of the proposed method is verified by both simulation and field experiments using GPR B-scan data. In addition, the experimental results show that the proposed method can achieve the AP with 96.9%. The code is available at https://github.com/yimaxwell/state-space-model-for-GPR-detection.git.