On-line Fabric-Defects Detection Based on Wavelet Analysis

This paper introduces a vision-based on-line fabric inspection methodology for woven textile fabrics. The current procedure for the determination of fabric defects in the textile industry is performed by humans in the off-line stage. The proposed inspection system consists of hardware and software components. The hardware components consist of CCD array camera, a frame grabber, and appropriate illumination. The software routines capitalize on vertical and horizontal scanning algorithms to reduce the 2-D image into a stream of 1-D data. Next, wavelet transform is used to extract features that are characteristic of a particular defect. The signal-to-noise ratio (SNR) calculation based on the results of the wavelet transform is performed to measure any defects. Defect detection is carried out by employing SNR and scanning methods. Learning routines are called upon to optimize the wavelet coefficients. Test results from different types of defect and different styles of fabric demonstrate the effectiveness of the proposed inspection system.

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