基于改进ORB-FLANN算法的工件图像识别方法OA

In view of the problems of low matching rate and long running time of traditional image recognition algorithms,an improved ORB-FLANN(Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors)based workpiece image recognition method is proposed.The feature description of ORB algorithm and im-age feature matching algorithm are modified to solve the disadvantages of traditional image recognition algorithm in the case of scale and rotation transformation and reduce the mismatching rate of matching.For the feature points detected by ORB algorithm,SURF(Speeded Up Robust Features)algorithm is used to add orientation information and com-plete the feature description,so as to obtain the feature points with rotation-scale invariance.FLANN algorithm is combined with bidirectional matching strategy for coarse matching of feature points.Finally,the progressive sampling-congruence algorithm is used to further eliminate the mismatched point pairs and complete the fine matc-hing.The experimental results show that compared with other methods,the improved algorithm can improve the matc-hing accuracy of 2.6%～18.8%and 29.5%～43.9%,respectively,when processing scale and rotation transform images,and the running time is within 4s,improving the efficiency and accuracy of workpiece image recognition.