Research on Low Contrast Feature Extraction and Registration Effect of Concrete Structure based on SuperGlue Algorithm

Abstract

In the aspect of low-contrast feature extraction and registration of concrete structure surface, traditional algorithms have some problems such as low computational efficiency, less feature extraction and low matching accuracy. The method based on deep learning has become a mainstream method at present, but the supervised learning method based on manual annotation has the problem that low contrast features cannot be marked. In view of this, it is necessary to study the most promising deep learning method based on graph convolution for progressive extraction and registration of low-contrast features of concrete structure surfaces. This paper uses Superpoint framework to solve the low contrast problem at the end of supervised learning. The indoor and outdoor test results show that the deflection curve trend of measuring points is basically consistent with that of the displacement meter, which indicates the robustness of feature point tracking based on SuperGlue, and further indicates that the method can be used as an effective technical reserve for deflection measurement of concrete structures.