Electronic Science and Technology ›› 2024, Vol. 37 ›› Issue (4): 55-61.doi: 10.16180/j.cnki.issn1007-7820.2024.04.008

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Workpiece Image Recognition Method Based on Improved ORB-FLANN Algorithm

ZHU Zhihao1, LU Zhixu1, GUO Yu2, GAO Zhi3   

  1. 1. School of Electrical Engineering,Yancheng Institute of Technology,Yancheng 224051,China
    2. School of Automation,Nanjing University of Technology,Nanjing 210000,China
    3. School of Information Engineering,Yancheng Institute of Science and Technology,Yancheng 224051,China
  • Received:2022-11-02 Online:2024-04-15 Published:2024-04-19
  • Supported by:
    National Natural Science Foundation of China(61973167);University-Level Scientific Research Project of Yancheng Institute of Technology(XJR2020041)

Abstract:

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 image 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 complete 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 matching. The experimental results show that compared with other methods, the improved algorithm can improve the matching 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.

Key words: image recognition, ORB algorithm, SURF algorithm, FLANN algorithm, bidirectional matching, PROSAC, rate of matching, workpiece image

CLC Number: 

  • TP391