Key Point Comparison of Breech Face Impression by Deep Learning Method

For the feature points such as peak, valley, saddle and ridge points from the roughness component of 3D breech face impression(BFI), the traditional feature point detection and matching methods are poor of precision due to the invalid areas caused by the bias or incomplete contact, and are not suitable for the BFI with striated textures. So, the self-supervised learning model was applied to feature point detection and the attentional graph neural network was used to feature point matching. For feature point detecting, the virtual image dataset was supervised trained, and then the pseudo-labels are generated by multi-scale transformation and self-supervised learning was carried out. For feature point matching, the attention mechanism graph neural network was used to establish the matching relationship between feature points. In order to reduce the interference of invalid areas, a dustbin layer was added to represent the feature points without any matching feature point. Confocal microscopy was used to collect the surface topography of BFI and the roughness component was extracted by filtering. The BFI with granular and striated textures were verified, and compared with the traditional method. The maximum matching rate of the known non-matching BFI was set as the threshold, and the known matching BFI can be completely distinguished from the known non-matching ones. It is suitable for the BFI with granular and striated textures, and without interfering with the invalid areas.