Application Of Fast Image Feature Detection Algorithm On Industrial Detection

The application of machine vision system in industrial inspection has become an urgent need in production,for it can improve the mechanical automation capabilities and productivity in both the manufacturing and printing industries.A high-speed feature detection method has been proposed in this paper to solve slow speed and poor stability problems of image detection method which is applied in a factory environment.It meets requirements of the real-time and accuracy industrial inspection.The main work of this paper includes:(1)According to the running environment of the production line and the features of machine vision system,a uniform light method based on wavelet is proposed in this paper to eliminate the effects of light source in machine vision system so that it can obtain a stable input image.(2)An iterative template registration method was proposed to ensure the speed of positioning the image,and the improved LBP method has been chosen to extract the whole contour features of the image to ensure the accuracy of positioning the image.(3)An ORB feature detection method and an match method based on KNN to meet the real-time requirement are adopted in this paper guaranteeing to calculate the similarity between the sample image and the template image with a similarity method based on blocks.In addition,an automatic detection system for industrial samples is designed based on the algorithm proposed in this paper.The system is used in collating machinery for printing industry.It has been tested that the maximum time consumed,according to the algorithm proposed in this paper for sample detection,is less than 250ms,and the accuracy rate is about 99.5%.Therefore,it can completely meet the real-time and accuracy requirements of industrial inspection.