DEVELOPING AN ALGORITHM FOR DEFECT DETECTION OF DENIM FABRIC: GABOR FILTER METHOD

In this study, an algorithm is developed by using Gabor filtering and double thresholding methods for fabric defect detection automatically. The defective area of the fabric image is accentuated, and the defect-free texture is attenuated as result of convolution operation with the Gabor filter. The image is then converted into binary form for by using double thresholding method. The noises are removed and the defective area is determined clearly by using dilation and erosion morphological operations. The boundary of the defect is labeled. A fabric defect image database consists of five defect types; warp lacking, weft lacking, hole, soiled yam and knot is formed. The database includes 30 different images for each type of defect and defect-free fabric samples. Thus, the algorithm is applied over 180 fabric images. All defective areas are detected with high success rates. The performance of the algorithm is given statistically.