Classification and identification of foreign fibers in cotton on the basis of a support vector machine

Foreign fibers in cotton have seriously affected the quality of cotton products. The classification and identification of foreign fibers in cotton is the foundation of cotton foreign fiber automated inspection. The paper takes the typical cotton foreign fibers in China’s textile enterprises as the research object, and acquires the images under simulated actual cotton processing. The various classification features are calculated and analyzed. The results show that aspect ratio, roundness, duty cycle and I1I1 are the effective features for classifying various foreign fibers. The paper puts forward a classifier of cotton foreign fibers based on a support vector machine. A Decision Tree Support Vector Machine (DTSVM) can not only avoid the non-separated region, but also improve the training speed while the training sample number gradually decreases, going through the decision tree. The DTSVM is to be used to identify the sorts of common foreign fibers in cotton. The experimental data set shows that the rates of identifying different kinds of foreign fibers are greater than 92% using the proposed DTSVM.