Automatic Density Measurement of Striped Woven Fabrics Based on Adaptive Filtering

Abstract

In order to reduce the interference of stripe signal on the density automatic measurement of woven fabric and extend the the application scope, an adaptive Gaussian Notch Filtering (GNF) algorithm based on Fast Fourier transform is proposed. In the pre-proceesing stage, the Fourier transform was utilized to transform the woven fabric bitmap to spectral image, as to obtain the spectral characteristics of the striped fabric. The improved GNF algorithm was used to identify the peak of stripe signal, which determined the bandwidth radius and locate the stripe bright area. Based on the fused Fourier and GNF spectral map, interference frequency information was accurately removed and spatial map was restored to approximate pure color fabric image. Finally, the Morlet wavelet is utilized to analysis the density of the striped printed fabric. The experimental results show that the average subjective-objective consistency rate (AS-OCR) of the designed sample is 99.34%, and the range is 97.33%∼100%. The AS-OCR of the actual sample is 98.73%, ranging from 95.00% to 100%. The proposed method can effectively obtain the density of stripe printed woven fabric and expand the application scope of fabric density automatic detection.