Intelligence control of on-line dynamic gray cloth inspecting machine system module design. II. Defects inspecting module design

This study aimed to establish a set of gray cloth defect inspection module using image processing technique, so as to develop a full intelligent online dynamic gray cloth defect automatic inspection system. Gray cloth defects to be recognized in this study included holes, stains, warp missing, spider web and weft missing. First use wavelet transform and co-occurrence matrix to find features of gray cloth defect image, next, use back-propagation neural network (BPNN) to make gray cloth defect classification and data output. BPNN was capable of solving nonlinear problems, thus assisted in enhancing defect recognition effect. As every defect to be inspected in this study varied in size and shape, so advantage of BPNN could be used as aid more than else. This study primarily utilized image processing technique to inspect gray cloth defects, not only in a faster speed than common visual inspection, but also eliminating arbitrary factors of inspectors in body and psychology during inspection, resulting in absolute objectivity. Finally, tension control module built in Part 1 and gray cloth defect inspection module built in this study were integrated, and a full intelligent online dynamic gray cloth defect automatic inspection system established. As validated by experiment result, the system established in this study could successfully recognize gray cloth defects, with total recognition rate amounting to 92.5 %.