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Bamboo strips, as assembling parts of sleeping mats, cushions and other decorative components, play an important role in humans’veryday life and social economy now. Therefore, quality control for bamboo strips production is very critical. Traditional manual sorting technology owns many disadvantages such as high production cost and low sorting accuracy. This work deals with an automatic sorting system for comprehensive defect detection of bamboo strips based on machine vision. Differing from the present feature extraction methods of the bamboo strip in image processing, contour features considering area and geometrical symmetry and texture feature considering average gradient are newly introduced. An experimental automatic sorting system is designed to verify the feasibility and superiority of the proposed comprehensive defect detection method. Experimental results show that total defect detection accuracy, contour defect detection accuracy, surface texture defect detection accuracy and sorting accuracy reach 99.1%, 98.33%, 95.2% and 95.125%, respectively. The designed sorting system finishes one time sorting in 197 ms with a comparable low-speed computation processor in laboratory and it can be utilized instead of three skilled workers in practice.
Wu JQ, Li ZY. International competitiveness of main bamboo and rattan commodities in China. Chinese Forestry Science and Technology 2009;8(2):55-62.
Wang XY, Liang DT, Deng WY. Surface grading of bamboo strips using multi-scale color texture features in eigenspace. Computer and Electronics in Agriculture 2010; 73:91-98.
Tajeripour F, Kabir E, Sheikhi A. Fabric defect detection using modifi ed local binary patterns. EURASIP Journal on Advances in Signal Processing 2008; 2008:1-12.
Liao S, Law M, Chung A. Dominant local binary patterns for texture classification. IEEE Trans on Image Process 2009;18(5):1107-1118.
Guo Z, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classi fi cation. IEEE Trans on Image Process 2010;19(6):1657-1663.
Ojala T, Pietikaeinen M, Harwood D. A comparative study of texture measures with classi fi cation based on feature distributions. Pattern Recognition 1996; 29(1):51-59.
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classi fi cation. IEEE Transactions on Systems, Man, and Cybernetics 1973;3(6):610-621.
Fahrurozi A, Madenda S, Ernastuti E, et al. Wood texture features extraction by using GLCM combined with various edge detection methods. Journal of Physics: Conference Series 2016; 725(1):012005.
Colgan MS, Bladeck CA, Feret JB, et al. Mapping savanna tree species at ecosystem scales using support vector machine classi fi cation and BRDF correction on airborne hyperspectral and lidar data. Remote Sensing 2012; 4:3462-3480.
Piuri V, Scotti F. Design of an automatic wood types classification system by using fl uorescence spectra. IEEE Transactions on Systems, Man, and Cybernetics 2010; 40:3.
Zhang P, Kai Z, Li ZW, et al. High dynamic range 3D measurement based on structured light: A review. Journal of Advanced Manufacturing Science and Technology 2021; 1(2): 2021004.
Muruganatham C, Jawahar M, Ramamoorthy B. Optimal settings for vision camera calibration. The International Journal of Advanced Manufacturing Technology 2009; 42:736-748.
Fu JH, Liu HD, He MQ, et al. A hand-eye calibration algorithm of binocular stereo vision based on multi-pixel 3D geometric centroid relocalization. Journal of Advanced Manufacturing Science and Technology 2022; 2(1): 2022005.
Erkan U, Goekrem L, Enginoglu S. Different applied median fi lter in salt and pepper noise. Computers & Electrical Engineering 2018; 28: 241-253.
Javadpour A, Mohammadi A. Improving brain magnetic resonance image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Journal of Biomedical Physics and Engineering 2016; 6(2):95-108.
Ojala T, Pietikaeinen M, Maenpaeae T. Multiresolution gray-scale and rotation invariant texture classi fi cation with local binary patterns. IEEE Transactions on Pattern Anal 2002; 24(7):971-987.
Ulaby FT, Kouyate F, Brisco B. Textural information in SAR images. IEEE Transactions on Geoscience and Remote Sensing 1986; 24(2): 235-245.
Chang CC, Lin CJ. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2007; 2(3): 1-30.
Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006; 27(8):861-874.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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