Detecting Underground Pipes and Void Models by GPR 3D Scanner with Unsupervised Semantic Segmentation

Detecting underground pipes and voids is vital for infrastructure safety. Using supervised learning for Ground-Penetration Radar (GPR) volumetric images faces difficulties when with limited unlabeled data. This paper introduces an unsupervised learning method for detecting underground objects from a GPR volumetric image without training dataset. The method employs singular value decomposition (SVD) for image enhancement, then merges textures and intensities using Gabor and Gaussian filters. A 3D-CNN model refined by graph-based supervoxels learning from the single 3D image, segments this image into a voxelwise label map. Following noise removal and binarization, a 3D binary underground-object map labels all voxels. Tested on a experimental field, the method outperformed previous method and identified 39 of 45 objects with 86.1% precision and a maximum IoU of 57.5%, emphasizing the efficacy of unsupervised learning in under-ground object segmentation.