Recognition for underground voids in C-scans based on GMM-HMM

Ground penetrating radar (GPR), as a fast, efficient, and non-destructive detection device, holds great potential for the detection of shallow subsurface environments, such as urban road subsurface monitoring. However, the interpretation of GPR echo images often relies on manual recognition by experienced engineers. In order to address the automatic interpretation of cavity targets in GPR echo images, a recognition-algorithm based on Gaussian mixed model-hidden Markov model (GMM-HMM) is proposed, which can recognize three dimensional (3D) underground voids automatically. First, energy detection on the echo images is performed, whereby the data is preprocessed and pre-filtered. Then, edge histogram descriptor (EHD), histogram of oriented gradient (HOG), and Log-Gabor filters are used to extract features from the images. The traditional method can only be applied to 2D images and pre-processing is required for C-scan images. Finally, the aggregated features are fed into the GMM-HMM for classification and compared with two other methods, long short-term memory (LSTM) and gate recurrent unit (GRU). By testing on a simulated dataset, an accuracy rate of 90% is obtained, demonstrating the effectiveness and efficiency of our proposed method.