Open AccessCorrelation inversion detection algorithm and imaging simulation of wood defects focused by stress signal based on Symlets wavelet
Author(s) -
Bin Zeng,
Xuan Li,
Zhuo Mao
Publication year - 2020
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 - 10.1088/1742-6596/1651/1/012085
Subject(s) - wavelet , inversion (geology) , computer science , algorithm , artificial intelligence , signal (programming language) , interference (communication) , pattern recognition (psychology) , computer vision , geology , telecommunications , paleontology , channel (broadcasting) , structural basin , programming language
Aiming at the problems of traditional wood natural defect detection methods such as difficult to remove interference signals, weak signal enhancement, and low defect detection accuracy, a wood natural defect detection method based on Symlets wavelet and inversion reconstructed signals is proposed. First, the defect information is extracted by multi-scale slice based on Sym3 wavelet. Then, the high-frequency abnormal signal components of the defect stress after reconstruction are “focused” correlated inversion detection and analysis, and the defect information extraction process based on the inversion “focusing” is realized. Finally, the characteristic signals of natural defects in wood are effectively separated to achieve high-precision detection and three-dimensional image reconstruction of the position and shape of the defects. This method has achieved good experimental simulation results, and the recognition accuracy of wood knots and other defects is over 97%.