Open AccessA Matching Algorithm T-AKAZE for Image Recognition of Hydroelectric Equipment Failure
Author(s) -
Jiayu Xu,
Xiaohui Ji,
Yan Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012003
Subject(s) - ransac , algorithm , matching (statistics) , computer science , robustness (evolution) , feature matching , orb (optics) , blossom algorithm , image matching , image (mathematics) , similarity (geometry) , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , mathematics , biochemistry , statistics , chemistry , linguistics , philosophy , gene
Aiming at the shortcomings of the current image-based fault identification algorithm for hydropower station equipment, such as low efficiency and poor accuracy, an improved image matching algorithm based on AKAZE, T-AKAZE was proposed. In this paper, AKAZE algorithm is firstly used to obtain image feature points. Then, the improved algorithm T-AKAZE is used for coarse matching of feature points. On the basis of Tanimoto coarse matching, RANSAC algorithm is used for fine matching to improve the algorithm accuracy. In order to verify the matching effect of the improved algorithm, ORB algorithm, AKAZE algorithm and ORB+Tanimoto algorithm were compared and analyzed. The experimental results show that the proposed algorithm has good robustness for image matching of different scales and similarity, and its timeliness also meets the application requirements, which provides convenience for the staff to identify the fault of hydropower station equipment.