Intelligent enhancement and identification of pipeline hyperbolic signal in 3D ground penetrating radar data

Concealed pipeline maintenance in aging residential areas faces a key challenge of discrepancies between existing data and reality. Ground-penetrating radar with dense, high-speed 3D monitoring capabilities can provide massive data, but effective analysis is difficult due to the presence of irrelevant information. To accurately extract target information, this paper first proposes a 3D data array block concept, which enhances the feature relevance of target data blocks while expanding the data volume. An energy density window method is also proposed to enhance horizontal cross-sectional pipeline signals. Furthermore, a model named PR3DCNN for pipeline recognition is developed based on 3D convolutional neural networks and residual modules. Experimental results demonstrate that PR3DCNN has a classification accuracy of 0.871 for pipelines. After strengthening with 3D data array blocks and the energy density window, the PR-EDW-B model achieves an accuracy of 0.900, and can also classify the pipeline material and calculate its orientation. •A method is proposed to enhance pipeline features by arraying 3D data.•An energy density window method is proposed to improve imaging clarity of pipeline.•A 3D CNN is established for pipeline identification and material classification.•The proposed method can also calculate the direction of concealed pipelines.