Due to the immaturity of printing technology, printing defect is an inevitable problem in industrial production. It will greatly affect the appearance and performance of the product if defects could not be found and dealt with in time. Defect detection often requires high real-time performance and registration rate. Especially in the case of angular shift or image translation, the algorithm needs to have good registration effect. Feature matching is an important part of image registration. In the actual detection process, the speed and quality of matching directly affect the results of image registration algorithm. Based on deep understanding of SIFT, SURF, ORB and GMS, in the case of different lighting environments, different shooting angles, scale changes and fuzzy images, the superposition effect, registration time, number of feature point pairs and registration rate of four typical feature matching algorithms above are compared on the open data set respectively. The robustness, speed, advantages and disadvantages as well as applicable conditions of those four algorithms are analyzed and summarized. And an appropriate algorithm is selected based on the actual defect detection task. Experimental results show that ORB algorithm has the characteristics of fast speed, high registration rate and strong robustness in different environments. So, it is adopted in the actual defect detection task. Actual defect detection results show that ORB algorithm can not only accurately frame the defect area of printed matter, but also well meet the real-time production requirements of defect detection.
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