Laser & Optoelectronics Progress, Volume. 58, Issue 22, 2230002(2021)
Comparison of Paint Classification Methods Based on Spectral Fusion
Figures & Tables(10)
Fig. 1. Four infrared spectra of 50 paint samples. (a) Original spectra; (b) first derivative spectra; (c) second derivative spectra; (d) third derivative spectra
Fig. 3. Overall classification and recognition rate of five paint samples under 10 spectral data models
Fig. 4. Statistical results of minimum classification errors of four kernel functions
Fig. 5. Recognition rate of paint samples from different spectral data sets by SVM
Fig. 6. The average classification and recognition rate of all kinds of samples by SVM
Table 1. Classification and recognition rate of paint samples for each spectral data when K=1
View tableTable 1. Classification and recognition rate of paint samples for each spectral data when K=1
Spectral type Accuracy /% Y1 Y2 Y3 Y4 Y5 Original spectra (OG) 14 100 88 84 14 1st derivative spectra (FD) 57 100 100 74 14 2nd derivative spectra (SD) 57 100 100 89 43 3rd derivative spectra (TD) 57 100 100 74 29 G1 29 100 100 79 14 G2 29 100 100 89 29 G3 43 100 100 84 29 G4 71 100 100 89 43 G5 57 100 100 79 29 G6 43 100 100 84 29 Average accuracy 45.7 100 98.8 82.5 27.3
Table 2. The recognition rate of training sets and test sets of five discriminant analysis models under different spectral data
View tableTable 2. The recognition rate of training sets and test sets of five discriminant analysis models under different spectral data
Spectral type Accuracy /% Wilks’ Lambda Unexplained variance Mahalanobis distance Smallest F ratio Rao’s V Training set Test set Training set Test set Training set Test set Training set Test set Training set Test set OG 94 78 94 88 94 88 94 86 92 76 FD 90 84 94 90 94 86 100 90 92 72 SD 96 90 96 90 100 96 100 90 90 84 TD 98 90 98 88 100 96 98 94 98 86 G1 94 88 92 86 100 98 100 90 92 72 G2 96 90 96 90 100 96 100 90 90 84 G3 98 88 96 88 100 96 100 92 98 86 G4 90 86 100 90 100 96 100 94 96 82 G5 96 84 96 88 98 94 100 100 88 80 G6 92 84 96 88 100 98 100 96 96 86
Table 3. Discriminant function characteristic table
View tableTable 3. Discriminant function characteristic table
Function Eigenvalue Canonical correlation Test of function Wilks’ Lambda P F1 90.092 0.994 1 through 4 0.000 0.000 F2 14.707 0.968 2 through 4 0.002 0.000 F3 11.233 0.958 3 through 4 0.028 0.000 F4 1.876 0.808 4 0.348 0.001
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Kunshan Gu, Jifen Wang. Comparison of Paint Classification Methods Based on Spectral Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2230002
Paper Information
Category: Spectroscopy
Received: Dec. 27, 2020
Accepted: Feb. 4, 2021
Published Online: Nov. 10, 2021
The Author Email: Jifen Wang (wangjifen58@126.com)
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