3D ground penetrating radar road underground target identification algorithm using time-frequency statistical features of data

Abstract

3D ground penetrating radar is the main detection technology used to locate underground targets on urban roads, and the current underground target intelligent identification algorithms for this technology rely on two-dimensional images. Relying only on radar image texture as the identification feature, due to the limitations of single dimensionality and single feature utilization, leads to significant errors of these algorithms in identifying underground targets. For the above problems, an underground target depth time-frequency statistical feature identification algorithm based on 3D convolution using raw radar data is proposed. The detailed changes and overall trends of underground targets are extracted by multi-size 3D receptive fields. Meanwhile, the depth time-frequency statistical features of multiple position slices in the horizontal, vertical, crossed-vertical, left-right diagonal, and front-back diagonal directions of radar data are constructed at different layers. Multi-dimensional time-frequency statistical features are input to a fully connected classifier, and the final result is obtained by combining the classification results of the seven directions through the Bagging mechanism. This algorithm expands the feature dimension and direction of judging underground targets and strengthens the feature representation ability. It makes full use of the 3D ground penetrating radar data information and reflects the features of underground space from multiple angles, which improves the identification accuracy of underground targets.