Wavelet analysis to fabric defects detection in weaving processes

The current procedure for the determination of fabric defects in the textile industry is performed by humans in the offline stage. The advantage of an online inspection system is not only defect detection and identification, but also quality improvement by a feedback control loop to adjust setpoints. This paper introduces a vision-based online fabric inspection methodology of woven textile fabrics. The proposed inspection system consists of hardware and software components. The hardware components consist of CCD array cameras, a frame grabber and appropriate illumination. The software routines capitalize upon vertical and horizontal scanning algorithms to reduce the 2-D image into a stream of 1-D data. The wavelet transform is used next 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. The defect declaration is carried out by employing SNRs 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