Multidirectional Enhancement Model Based on SIFT for GPR Underground Pipeline Recognition

Abstract

The recognition of underground pipelines is an important in urban areas. As an efficient and non-destructive recognition method, ground penetrating radar (GPR) has been increasingly applied in the recognition of underground pipelines. With the growing volume of GPR data, there is an urgent need for automatic recognition. However, due to the complexity of the subsurface environment, existing automatic recognition methods still have drawbacks such as low accuracy, poor robustness, and the requirement for large training datasets. An underground pipeline recognition model for GPR that combines scale-invariant feature transform (SIFT) and support vector machine (SVM) is proposed in this article. The model is based on the fact that there are scale-invariant keypoints at the tops of hyperbolas. First, SIFT is used to identify scale-invariant keypoints in the image. These keypoints undergo symmetry assessment and feature enhancement. Subsequently, SVM is employed to filter out the keypoints located at the tops of the hyperbolas. Finally, keypoints located on the same hyperbola are clustered to obtain the recognition results. The model improves the original SIFT method by modifying the calculation of the blur coefficients in the Gaussian pyramid layers. It also employs manually designed feature enhancement methods when constructing the feature descriptors. Additionally, we have introduced methods such as symmetry judgment to further enhance the model’s accuracy. The results indicate that the proposed method exhibits superior recognition performance for the field data even with very limited training samples.