Automatic fabric defect detection in textile images using a labview based multiclass classification approach

Nowadays the detection of fabric defects is an active research topic to detect and resolve the difficulties faced in processing fabric in printing and knitting in textile industries. The traditional approach of visual screening of human fabrics is exceedingly time consuming and it is not reliable as it is much susceptible to human errors. There are two major issues in defect inspection like defect identification and classification in fabric. Automatic identification of defects is quite important in the current scenario. For enhancing the quality of the fabric, this paper proposes a Texture Defect Detection (TDD) algorithm. This TDD algorithm utilizes pre-processing for the extraction of luminance plane and Discrete wavelet frame decomposition for dividing the image into several subbands with same resolution as input image. Statistical features are extracted using Gray Level Co-occurrence Matrix and these features are applied to Support Vector Machine for classifying the defective images. This improves the quality of texture segmentation and classification of visible defects. The experimental setup is done with the fabric conveyor and three high resolution industrial cameras acA4600-7gc for covering the entire width of the fabric while running. This TDD algorithm is developed under LabVIEW platform. Textile Texture Database (TILDA) multi-class dataset is used for testing the proposed algorithm. This algorithm is tested for 4 different classes of fabric defects including 2800 defective and 400 non defective fabric images. The success rate of detection of fabric defect is 96.56% with the images from the database. The validation results with real time fabric images show 97% of accuracy in the detection of defects in fabric images.