An Improved FAsT_Match Algorithm for Micro Parts Detection

Through analysis the characteristics of micro parts such as multi-categories, high detection frequency and similar shape in the classification process, based on the FAsT_Match algorithm, an improved algorithm of template matching recognition is proposed which is a Grid Region Optimized FAsT_Match (GRO FAsT_Match for short). Firstly, the method of gray level adjustment, global threshold image segmentation, boundary tracking and denoising is used to extract the smallest rectangle of the target part image as ROI area. Secondly, by calculating the scale relationship between ROI region and template image, the step sizes and limits of grid parameters for translation and scaling transformation are optimized. In order to improve the discrimination of normalized SAD distance for similar parts, uniform sampling of template image is adopted. The experimental data show that this algorithm features fast, precise, clear distinguish of similar parts, and meets the requirements of micro parts classification and detection. It has practical significance to improve the assembly efficiency of micro parts.