A COMPUTER VISION APPROACH FOR FABRIC DEFECTS INSPECTION
In the textile production, there may appear many fabric defects. To fabric defects, there are a lot of image-based inspection techniques: Fourier transform, Sobel algorithm of edge inspection, fast Fourier transform (FFT) et. However, Wavelet transform is a kind of multiresolution algorithm, and its multiresolution character corresponds to time-frequency multiresolution of human vision. The result of the research indicates that wavelet transform gives better results than the other traditional methods. So in this article, we use wavelet transform and BP neural network together to inspect and classify the fabric defects. A plain white fabric is adopted as the sample, and the distinguishing defects are oil stains, warplacking, and weft-lacking. An area camera with 256×256 resolution is used in the scheme, a grabbed image is transmitted to a computer for wavelet transform, and then the corresponding image data are then used in BP neural network as input. The result shows that the fabric defects’ classification rate can be up to 95% with above method.
fabric defect wavelet transform inspection computer vision.
Liu Shu-Guang Qu Ping-Ge
Xi’an Polytechnic University, Xi’an 710048 China
国际会议
International Technology and Innovation Conference 2009(2009技术与创新国际学术会议)
西安
英文
1-4
2009-10-12（万方平台首次上网日期，不代表论文的发表时间）