A Cylinder Head Identification and Localization Method Using OFS-GMS Feature Matching and Minimum Surrounding Contour

Rotational offsets, oil reflections, and noise interference pose significant challenges to cylinder heads identifying and localizing. In this article, we propose a recognition and localization method that integrates oriented FAST (oFAST), speeded-up robust features (SURFs), and grid-based motion statistics (GMSs) with minimum surrounding contours. First, the oFAST extracts feature points from the cylinder head image, and a 64-D SURF descriptor is generated for these feature points to obtain initial matching results. Second, the GMS algorithm performs grid segmentation and eliminates the mismatched points. Then, an adaptive threshold key point evaluation method is applied to refine the feature points and eliminate residual edges. Finally, the pose information of the cylinder head is obtained by background suppression and minimum surrounding contour (BSMSC) method. We conduct cylinder head recognition and localization experiments on the self-developed sorting robot experimental platform. Experimental results show that the OFS-GMS matching recognition algorithm has an average recognition accuracy of 99%. The BSMSC-based pose estimation method operates with an average processing time of 48.14 ms and a maximum angular error of less than 1.5°.