Application of image recognition based on SVM in part sorting system
                                                                          SUN Xiaoquan, ZOU Liying
                                             (Zhijiang College, Zhejiang University of Technology, Shaoxing 312030, China)

Abstract: Aiming at solving the problem that the parts with the uniform appearance shape and mass distribution were hard to realize automatic material feeder under the directional requirement, the analysis and selection of the image acquisition, image processing, image feature extraction and pattern recognition to the parts were made, and the parts sorting system based on the image recognition was proposed. Wavelet transformwas used on the images that were acquired by installing cameras on the exit of the vibrating disk, and then the disturbance filtering and dimensionality were performed. Principal component analysis (PCA) was further applied to reduce dimensions and extract the features of images, and support vector machine (SVM) was used on the input vectors for pattern recognition. The location of the parts was determined by carrying out SVM on the input vectors, and meanwhile the parts that do not comply with the requirement were propelled by the drive set, for the purpose of realizing automatic feeding in the next procedure. The experimental results indicate that when the number of training samples reaches 20, the recognition accuracies of the two kinds of parts are 100%, and the recognition time of one single part is within one second, which can meet the actual production requirements.

Key words: part sorting; wavelet transform; principal component analysis (PCA); support vector machine (SVM)