Abstract
A visual technology to identify the punching points of a textile vamp to replace manual punching by machinery is proposed in this paper. This could solve several problems relating to the manual punching of textile vamp, such as high manual-punching strength, low efficiency, and poor punching accuracy. Unsharp mask guided filtering was adopted, to enhance the details of the textile-vamp punching points, considering the edge point position, gradient phase, and edge significance of the punching points, in building the template matching similarity measure function. A partial Hausdorff distance was proposed to sum and average values, to improve the degree of matching of punching point shape defects. A local search area of punching points to improve identification efficiency and punching evaluation criteria was established to evaluate the punching effects. The results showed that the matching similarity of the complete boundary was above 0.9. Positioning accuracy was 0.43 mm in the x-direction, 0.38 mm in the y-direction, and repeat positioning accuracy was 0.09 mm. The center average relative error was 5.03% and the relative error of the radius was 7.81%. Identification timeliness increased with the rotation angle, template size, and the number of punching points. When the rotation angle was between –180° and 180° and the number of punching points was 24, identification timeliness was 830 ms, which met productivity requirements. Textile-vamp punching grades A to D qualified, however, grades E and F were unqualified for punching.
Published Version
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